Identifying geographical regions serviced by hospitals to assess laboratory-based outcomes
Bibliographic record
Abstract
OBJECTIVE: To define geographical regions (forward sortation areas; FSAs) in Southwestern Ontario, Canada from which patients would reliably present to a hospital with linked laboratory data if they developed adverse events related to medications dispensed in outpatient pharmacies. DESIGN: Descriptive research. SETTING: Forty-five hospitals in Southwestern Ontario, Canada, from 2003 to 2009. PARTICIPANTS: Patients aged 66 years and older who received an outpatient prescription for any drug and presented to the emergency department in the subsequent 120 days. MAIN OUTCOME MEASURE: The proportion of patients in a given FSA presenting to an emergency department at a hospital with linked laboratory data versus a hospital without linked laboratory data. To be included in the catchment area at least 90% of emergency department visits in an FSA must have occurred at laboratory-linked hospitals in a given year. RESULTS: Over the study period, there were 649 713 emergency department visits by patients with recent prescription claims from pharmacies in 1 of 118 FSAs. In total, 141 302 of these patients presented to an emergency department at a laboratory-linked hospital. For the year 2003, 12 FSAs met our criteria to be in the catchment area and this number grew to 25 FSAs by the year 2009. CONCLUSIONS: The relevant geographical regions for hospitals with linked laboratory data have been successfully identified. Studies can now be conducted using these well-defined areas to obtain reliable information on the incidence and absolute risk of presenting to hospital with laboratory abnormalities in older adults dispensed commonly prescribed medications in outpatient pharmacies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".