An Estimate of the Proportion of Symptoms Reported in Self-Administered Questionnaires That Are Captured as Adverse Drug Events in an Observational Database
Bibliographic record
Abstract
PURPOSE: This study was conducted to determine how frequently self-reported symptoms are captured as adverse drug events (ADEs) during chart abstraction. METHOD: We studied Ontario Cohort Study (OCS) participants attending the Toronto Hospital Immunodeficiency Clinic and compared OCS data on ADEs collected semi-annually through chart review and a self-administered questionnaire, completed on up to three occasions, which asked about the frequency, severity, and chronicity of symptoms including diarrhea, nausea, fatigue, and changes in body shape. Among 64 participants who completed the questionnaires, the median age was 42 years, the median time since HIV diagnosis was 9 years, 84% were male, 58% were men who had sex with men, 70% had viral load levels below 50 copies/mL, and the median CD4 was 422 cells/mm3. All patients were taking antiretroviral therapy. RESULTS: The median (interquartile range) number of symptoms per participant reported on the questionnaire at the first visit was 3 (1-5). The most common symptoms reported by patients were diarrhea (58%), headache (59%), difficulty sleeping (52%), dry skin (53%), and changes in body shape (52%). The median number of ADEs during the study period per participant in OCS was 1 (0-2). Of 345 symptoms identified on the questionnaire, 16% were reported as ADEs in the OCS. CONCLUSION: Although some symptoms were correctly not classified as ADEs as they were not related to antiretroviral medication, others may have been missed due to incomplete reporting to the physician, incomplete physician recording, or errors in chart extraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".