Constructing treatment episodes from concomitant medication logs: a prospective observational study
Bibliographic record
Abstract
OBJECTIVES: To describe an approach using concomitant medication log records for the construction of treatment episodes. Concomitant medication log records are routinely collected in clinical studies. Unlike prescription and dispensing records, concomitant medication logs collect utilisation data. Logs can provide information about drug safety and drug repurposing. DESIGN: A prospective multicentre, multicohort observational study. SETTING: Twenty-one clinical sites in the USA, Europe, Israel and Australia. PARTICIPANTS: 415 subjects from the de novo cohort of the Parkinson's Progression Markers Initiative. METHODS: We construct treatment episodes of concomitant medication use. The proposed approach treats temporal gaps as a stoppage of medication and temporal overlaps as simultaneous use or changes in dose. Log records with no temporal gaps were combined into a single treatment episode. RESULTS: 5723 concomitant medication log records were used to construct 3655 treatment episodes for 65 medications. There were 405 temporal gaps representing a stoppage of medication; 985 temporal overlaps representing simultaneous regimens of the same medication and 2696 temporal overlaps representing a change in dose regimen. The median episode duration was 37 months (IQ interval: 11-73 months). CONCLUSIONS: The proposed approach for constructing treatment episodes offers a method of estimating duration and dose of treatment from concomitant medication log records. The accompanying recommendations guide log data collection to improve their quality for drug safety and drug repurposing.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".