Stroke surveillance in Manitoba, Canada: Estimates from administrative databases
Bibliographic record
Abstract
This study investigated the use of population-based administrative databases for stroke surveillance. First, a meta-analysis was conducted of four studies, identified via a PubMed search, which estimated the sensitivity and specificity of hospital data for ascertaining cases of stroke when clinical registries or medical charts were the gold standard. Subsequently, case-ascertainment algorithms based on hospital, physician and prescription drug records were developed and applied to Manitoba's administrative data, and prevalence estimates were obtained for fiscal years 1995/96 to 2003/04 by age group, sex, region of residence and income quintile. The meta-analysis results revealed some over-ascertainment of stroke cases from hospital data when the algorithm was based on diagnosis codes for any type of cerebrovascular disease (Mantel-Haenszel Odds-Ratio [OR] - 1.70 [95% confidence interval (CI): 1.53 - 1.88]). Analyses of Manitoba administrative data revealed that while the total number of stroke cases varied substantially across the algorithms, the trend in prevalence was stable regardless of the algorithm adopted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".