BACKGROUND AND OBJECTIVES

The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards.

METHODS

A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated.

RESULTS

With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices.

CONCLUSIONS

qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.

Human ; Tuberculosis ; Diagnostic Imaging ; Deep Learning

OBJECTIVE

This meta-analysis aimed to evaluate the diagnostic capability of machine learning (ML) models in predicting stone-free status following percutaneous nephrolithotomy (PCNL).

METHODS

A comprehensive literature search was conducted across MEDLINE, Embase, Scopus, Cochrane, Google Scholar and supplementary databases was undertaken until June 2023. Inclusion criteria were English publications assessing the sensitivity and specificity of ML in predicting post PCNL stone-free status. Studies on non-human subjects or with incomplete data sets were excluded. Quality assessment utilized the Cochrane Risk of Bias Tool. Pooled sensitivity, specificity, and other diagnostic metrics were calculated using Meta-Disc 1.4 software.

RESULTS

Of the 65 initial articles, 5 met the inclusion criteria, representing a total of 1,773 participants. The accuracy of ML models ranged from 44% to 94.8%. The pooled sensitivity and specificity were 0.60 (95% CI [0.57, 0.63]) and 0.87 (95% CI [0.84, 0.89]), respectively. The pooled positive likelihood ratio was 4.69 (95% CI [3.82, 5.77]) and the negative likelihood ratio was 0.45 (95% CI [0.41, 0.48]). The diagnostic odds ratio was 10.93 (95% CI [8.35, 14.33]). The area under the curve (AUC) stood at 0.9372, signifying an excellent diagnostic performance.

CONCLUSION

Machine learning models demonstrate significant potential in accurately predicting stone-free status post-PCNL. However, the small number of included studies, retrospective designs, and heterogeneity in ML approaches limit generalizability. Standardized definitions, larger multicenter datasets, and prospective validation are required before routine clinical adoption.

Human ; Male ; Female ; Meta-analysis ; Artificial Intelligence ; Machine Learning ; Nephrolithotomy, Percutaneous

BACKGROUND

Outcomes-Based Education (OBE) in the Philippines has shifted perioperative training from numeric case quotas toward demonstrated competencies, with simulation increasingly used to address limited operating room (OR) exposure.

OBJECTIVE

This study determined the level of perioperative clinical performance among senior (4th year) nursing students and had also examined associations with four influencing domains: teaching–learning, interpersonal, student-related, and environmental.

METHODS

A descriptive–correlational study was conducted in AY 2023–2024 across higher education institutions in Eastern Visayas. A universal sample of 280 fourth-year BSN students who met minimum perioperative case requirements participated via online and paper surveys. Aresearcher-developed, expert-validated instrument that was anchored on CHED outcomes and PRC–BON guidelines was used to assess 11 competence domains in order to perceive influencing factors. Internal consistency was excellent (performance α = .987; factors α = .944). Descriptive statistics summarized competence; while Fisher’s Exact Test was used to assess associations (p < .05).

RESULTS

Competence was strongest in aseptic technique, patient safety, teamwork, documentation, and ethical–legal responsibilities. Lower ratings were noted for surgical skin preparation, anesthesia assistance, patient transport, medication safety, health education, and quality improvement. Among the four domains, only environmental factors (resources, workflow support, safety culture) were significantly associated with performance (Fisher’s Exact, p = .013).

CONCLUSION

Senior nursing students demonstrated strong technical and ethical perioperative competence but showed persistent gaps in less-frequent and higher-order competencies. Environmental supports in the OR decisively shaped performance. Programs should scale simulation for under-practiced tasks, strengthen mentorship, and explicitly integrate health education and quality improvement within perioperative training.

Human ; Learning ; Education ; Students, Nursing ; Mental Competency

Practice-based learning is the key objective of postgraduate education. COVID-19 has revealed that medical institutions may need to adopt adaptive strategies to guide their students. The aim of this study is to describe the perception of Philippine medical clerks towards their surgical preparedness with an Enriched Virtual Mode (EVM)-Hybrid Learning during the pandemic. A cross-sectional survey was conducted among 176 fourth-year students using a 21-item 4-point-Likert questionnaire. Descriptive analysis showed that students sustained a strong enthusiasm for surgery (composite mean = 2.83 ± 0.62), with the highest ratings given to skill-oriented subjects, such as practical minors (3.05 ± 0.82) and clinical surgery (3.03 ± 0.78). Preparedness was similarly high (3.17 ± 0.46): practice was regarded as essential (3.50 ± 0.68) and operating-room exposure useful (3.22 ± 0.68), though time for hands-on practice was adequate (2.84 ± 0.74). Preference scores revealed a desire for richer tactile experience (3.36 ± 0.37), with scrubbing, suturing and live surgery observation receiving most support (>3.50). Overall satisfaction reached a moderate-to-high level (2.99 ± 0.48) but lagged behind interest and preparedness, indicating that limited physical immersion tempered fuller contentment. These suggest that while a blended curriculum can preserve enthusiasm and sense of readiness, emphasis on protected skills laboratories and increased exposure to the operating room may be needed to translate conceptual competence into experiential fulfillment.

Human ; Male ; Female ; Young Adult: 19-24 Yrs Old ; Cross-sectional Studies ; Education ; Curriculum ; Perception ; Observation ; Schools, Medical ; Personal Satisfaction ; Learning ; Mental Competency ; Laboratories ; Pandemics

BACKGROUND

Hypertension is a major contributor to cognitive decline, and dementia is an increasing public health concern in the Philippines. Despite evidence linking these conditions, the awareness of dementia risk remains limited. Broader modifiable factors—such as nutrition, physical activity, smoking, alcohol use and sleep—also influence dementia risk but are not consistently emphasized in health education for hypertensive adults.

OBJECTIVE

To comprehensively assess the dementia risk awareness of hypertensive Filipino adults residing in the Philippines.

METHODS

An adapted questionnaire will gather data on dementia risk awareness among hypertensive Filipino adults. Phase I involves distributing the questionnaire via Google Forms on social media and collecting informed consent, the Personal Data Sheet (PDS), Dementia Knowledge Assessment Scale (DKAS) responses and self-reported modifiable risk factors from the McCance Brain Care Score (BCS). Phase II consists of quantitative analysis using descriptive statistics, including sub-analyses assessing correlations between dementia risk awareness and secondary measures.

EXPECTED RESULTS

At least 384 responses from hypertensive Filipino adults are anticipated, allowing classification into dementia risk–aware or dementia risk–unaware groups using DKAS thresholds. Exploratory analyses will describe potential associations between dementia risk awareness and selected modifiable risk factors

Human ; Male ; Female ; Adolescent: 13-18 Yrs Old ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Middle Aged: 45-64 Yrs Old ; Adult ; Awareness ; Dementia ; Learning ; Philippines ; Risk

BACKGROUND

Six-pyruvoyl-tetrahydrobiopterin synthase (6-PTPS) deficiency is an inherited metabolic disorder which results in tetrahydrobiopterin (BH4) deficiency causing hyperphenylalaninemia.

OBJECTIVE

This study aimed to describe the clinical, biochemical, and radiologic profiles, and neurologic and developmental outcomes of patients diagnosed with 6-pyruvoyl tetrahydrobiopterin (PTPS) deficiency through newborn screening and confirmed by BH4 loading test, pterin analysis, and gene sequencing who were following-up with the metabolic team.

METHODS

The research was a single-center descriptive case series study design that was done at the Philippine General Hospital, a tertiary government hospital. The clinical, biochemical, radiologic profiles and neurodevelopmental evaluation of each patient were described.

RESULTS

Nine patients from 1 year 2 months to 14 years 5 months of age were enrolled in the study. Clinical manifestations before treatment were hypotonia, poor suck, and seizure. The most common clinical manifestation even after treatment initiation was seizure. The mean phenylalanine level on newborn screening was 990.68 umol/L, but after treatment was started, mean levels ranged from 75.69 to 385.09 umol/L. Two of the patients had focal atrophy of the posterior lobe on brain imaging. Pathogenic variants on molecular analysis were all missense, with two predominant variants, c.155A>G and c.58T>C. Eight of the nine patients had varying degrees of developmental delay or intellectual disability, while the remaining patient had signs of a learning disorder.

CONCLUSION

Newborn screening has played a crucial role in the early identification and management of patients with hyperphenylalaninemia due to 6-PTPS deficiency. Confirmation of diagnosis through determination of DHPR activity, urine pterins and/or molecular analysis is necessary for appropriate management. However, despite early initiation of treatment, neurodevelopmental findings of patients with 6-PTPS deficiency were still unsatisfactory.

Human ; Infant: 1-23 Months ; Child Preschool: 2-5 Yrs Old ; Child: 6-12 Yrs Old ; Adolescent: 13-18 Yrs Old ; Learning Disorders ; Brain ; Diagnosis

BACKGROUND AND OBJECTIVES

One of the effects of the COVID-19 pandemic on medical education is an increased awareness and use of social media (SocMed) to facilitate learning. However, literature on the use of SocMed in medical education has focused primarily on educator-led teaching activities. Our study aimed to describe SocMed initiatives that were student-led, particularly for information dissemination and peer collaborative learning, and to elicit perceptions of medical students towards such activities.

METHODS

An online survey on SocMed usage in medical education was sent to all first- and second-year medical students at the University of the Philippines Manila College of Medicine from October to December 2021. The questionnaire collected data on demographics, SocMed habits and preferences, and perceived advantages and disadvantages of SocMed. Descriptive statistics were calculated while the free-text responses were grouped into prominent themes and summarized.

RESULTS

We received a total of 258 responses (71%) out of 361 eligible participants. Overall, 74% found SocMed platforms to be very and extremely helpful; 88% recommended its continued use. The most popular SocMed platforms for different tasks were as follows: Discord for independent study groups and for conducting peer tutoring sessions; Facebook Messenger for reading reminders; Telegram for reading announcements related to academics and administrative requirements, and for accessing material provided by classmates and professors.

CONCLUSION

The high uptake of SocMed among medical students may be attributed to its accessibility and costefficiency. The use of a particular SocMed platform was dependent on the students’ needs and the platform's features. Students tended to use multiple SocMed platforms that complemented one another. SocMed also had disadvantages, such as the potential to distract from academic work and to become a source of fatigue. Educators must engage with students to understand how SocMed platforms can be integrated into medical education, whether in the physical or virtual learning environment.

Human ; Education, Medical, Undergraduate ; Social Media ; Online Learning ; Education, Distance

This reflective paper explored the philosophical foundations of lifelong learning and impactful research in the field of nursing. Anchored in personal experience and supported by scholarly literature, it illustrated the transformative power of continuous learning, the cultivation of research competence, and the moral responsibility of contributing meaningfully to society. A nurse researcher's journey is not defined by awards or accomplishment but by an unwavering dedication to knowledge creation, community involvement, and evidence-based practice. The "emptying one's cup" metaphor embodies intellectual humility, a mindset that keeps the mind open to learning, self-improvement, and meaningful service throughout one's career.

Human ; Lifelong Learning ; Education, Continuing ; Nursing Research ; Reflective Practice ; Cognitive Reflection

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography

INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency department (ED) physicians on a binomial classification task. METHODS: A total of 1,314 paediatric elbow lateral radiographs (patient mean age 8.2 years) were retrospectively retrieved and classified based on annotation as normal or abnormal (with pathology). They were then randomly partitioned to a development set (993 images); first and second tuning (validation) sets (109 and 100 images, respectively); and a test set (112 images). An artificial intelligence (AI) model was trained on the development set using the EfficientNet B1 network architecture. Its performance on the test set was compared to that of five physicians (inter-rater agreement: fair). Performance of the AI model and the physician group was tested using McNemar test. RESULTS: The accuracy of the AI model on the test set was 80.4% (95% confidence interval [CI] 71.8%-87.3%), and the area under the receiver operating characteristic curve (AUROC) was 0.872 (95% CI 0.831-0.947). The performance of the AI model vs. the physician group on the test set was: sensitivity 79.0% (95% CI: 68.4%-89.5%) vs. 64.9% (95% CI: 52.5%-77.3%; P = 0.088); and specificity 81.8% (95% CI: 71.6%-92.0%) vs. 87.3% (95% CI: 78.5%-96.1%; P = 0.439). CONCLUSION The AI model showed good AUROC values and higher sensitivity, with the P-value at nominal significance when compared to the clinician group.
Humans ; Deep Learning ; Child ; Retrospective Studies ; Male ; Female ; Radiography/methods* ; ROC Curve ; Elbow/diagnostic imaging* ; Neural Networks, Computer ; Child, Preschool ; Elbow Joint/diagnostic imaging* ; Emergency Service, Hospital ; Adolescent ; Infant ; Artificial Intelligence