Additional file 2 of Detection of adverse drug events in e-prescribing and administrative health data: a validation study
Bibliographic record
Abstract
Additional file 2: Appendix 1. Documented adverse effects of antidepressant and antihypertensive medications. Appendix 2. Expanded, Therapeutic Class-Targeted ICD Code Set. Appendix 3. Standard ICD Code Set. Appendix 4. Characteristics of consented patients, patients who declined consent, patients who were not dispensed the study drug, patients who were dispensed the study drug, and patients who were included in the final analysis. Appendix 5. Symptoms reported during interview, overall and by study drug therapeutic class, and among patients who reported all, none, or some of their symptoms to their physician. Appendix 6. Potential medication-related adverse effects experienced by antidepressant and antihypertensive users based on expanded, therapeutic class-targeted codeset. Appendix 7. Details of treatment changes among patients with a potential ADE based on e-prescribing data. Appendix 8. Characteristics of patients who reported all, none, or some of the symptoms they indicated in the interview to their physician. Appendix 9. Severity of symptoms experienced, as reported during interviews, by patients who reported all, none, or some of their symptoms to their physician.
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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 | 0.003 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.596 | 0.059 |
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".