Cognitive Dysfunction and Learning Implications in Medical Students With Depressive Symptoms: Electrophysiological Evidence From P300 Event-Related Potentials
Bibliographic record
Abstract
INTRODUCTION: Major depressive disorder is highly prevalent among medical students and strongly associated with cognitive dysfunctions. OBJECTIVE: To compare neuropsychological and electrophysiological profiles (P300 parameters) of medical students with and without depressive symptoms. METHODS: A cross-sectional and comparative study was conducted with 140 second-year medical students. Depressive symptoms were assessed with the Patient Health Questionnaire-9. Cognitive performance was evaluated with the Montreal Cognitive Assessment (MoCA) and CogniFit (CogniFit Inc., San Francisco, CA, USA) computerized tests. Event-related potentials were recorded through a standard auditory oddball paradigm, analyzing N100, N200, and P300 latency and amplitude. Statistical analyses included independent sample t-tests and analysis of variance, with significance set at p < 0.05. Effect sizes (Cohen's d) were reported for all group comparisons, and appropriate corrections for multiple comparisons were applied to control type I error. RESULTS: Students with depressive symptoms exhibited slower response time (p < 0.01), processing speed (p = 0.01), impaired contextual memory (p =.01), short-term memory (p = 0.01), working memory (p = 0.01), focused attention (p = 0.01) and perception domain( p = 0.03). On the MoCA, lower in abstraction (p = 0.03), delayed memory recall (p < 0.01), and total MoCA score (p = 0.008). Event-related potentials analysis revealed significantly prolonged latencies for N100, N200, and P300 (all, p < 0.01), and decreased amplitudes in N100, N200, and P300 (p < 0.05). Prolonged event-related potentials latencies (particularly P300) correlated negatively with performance on processing speed (r = -0.39, p < 0.001), focused attention (r = -0.32, p < 0.001), and delayed recall (r = -0.31, p < 0.001). CONCLUSIONS: Medical students with depressive symptoms demonstrate specific cognitive impairments and altered event-related potential markers, reflecting reduced attentional efficiency and information processing. Combining computerized neuropsychological testing with electrophysiological measures may provide a feasible screening pathway for cognitive vulnerability among medical trainees and guide the development of preventive and educational interventions within medical curricula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".