Positive Predictive Value of Primary Subarachnoid Hemorrhage Diagnoses on Death Certificates
Bibliographic record
Abstract
PURPOSE: Epidemiological studies of primary subarachnoid hemorrhage (pSAH) frequently include population-based death registries for case finding. The positive predictive value of pSAH diagnoses in death registries is unknown. METHODS: This cross-sectional study identified all people in Ontario, Canada with pSAH listed as a cause of death between 2013 and 2017. pSAH was classified as "very likely" if diagnosis of pSAH was confirmed by autopsy, there was a previous hospitalization where pSAH probability exceeded 85% or death was preceded within a week by an emergency room visit where pSAH probability exceeded 25%. pSAH was classified as "very unlikely" if previous cerebrovascular imaging had never been done. Remaining cases were classified as "pSAH status unknown". RESULTS: 1,613 deaths attributed to pSAH were identified (mean 322/year). pSAH classification frequencies were as follows: very likely 528 (32.7%); very unlikely 433 (26.8%); and status unknown 652 (40.4%). CONCLUSION: We found that a quarter of pSAH cases in our province's death registry were very unlikely to be true pSAH while 40% had unknown veracity. These data should be considered when using death registries for pSAH case finding.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".