Linked administrative health data on prehospital olanzapine administration by paramedics in Winnipeg, Canada: Challenges and opportunities
Bibliographic record
Abstract
ObjectiveOlanzapine is an antipsychotic drug used in emergency departments to treat methamphetamine intoxication. Our study aim is to examine whether prehospital olanzapine administration by paramedics in Winnipeg, Canada, improves outcomes for individuals experiencing methamphetamine intoxication. However, the ‘real-world’ nature of the administrative data has presented several challenges. ApproachFirst, we needed to determine whether individuals experiencing methamphetamine intoxication received or did not receive olanzapine. The paramedic records identify whether olanzapine was administered but not whether individuals were considered for olanzapine without receiving it. To address this, we manually reviewed the unstructured narratives from the paramedic assessments. 8000+ records were independently evaluated by two experienced paramedics to determine eligibility. Disagreements were resolved by a paramedic-educator. Second, we discovered that ~40% of paramedic records for 2019 did not link correctly to other health data. We conducted a sensitivity analysis to assess the impact of excluding records with incorrect linkage, but the loss of records in the most critical year of assessment led us to reject this approach. Instead, we revisited the original data linkage to identify and correct the cause of the errors. ResultsWe have constructed a cohort that allows us to compare treated (n=222) and untreated (n=205) individuals experiencing methamphetamine intoxication and provides enough statistical power to assess the impact of prehospital olanzapine treatment on hospital outcomes. Conclusion & ImplicationsAdministrative data from the real world are powerful tools for research with potential to show important health impacts, but their use requires creative thinking to overcome unexpected data challenges.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".