The Registry of Canadian Stroke Network : an evolving methodology.
Bibliographic record
Abstract
Stroke registries can provide information on evidence-based practices and interventions, which are critical for us to understand how stroke care is delivered and how outcomes are achieved. The Registry of Canadian Stroke Network (RCSN) was initiated in 2001 and has evolved over the past decade. In the first two years, we found it extremely difficult to obtain informed consent from the patient or surrogate which led to selection biases in the registry. Subsequently (2003 onwards), under the new health privacy legislation in Ontario, Canada, the RCSN was granted special status as a "prescribed registry" which allowed us to collect data on all consecutive patients at the regional stroke centres without consent. The stroke data was encrypted and all personal contact information had been removed, therefore we could no longer conduct follow- up interviews. To obtain patient outcomes after discharge, we linked the non-consent-based registry database to population-based administrative databases to obtain information on patient mortality, readmissions, socioeconomic status, medication use and other clinical information of interest. In addition, the registry methodology was modified to include a periodic population-based audit on a sample of all stroke patients from over 150 acute hospitals across the province, in addition to continuous data collection at the 12 registry hospitals in the province. The changes in the data collection methodology developed by the RCSN can be applied to other provinces and countries.
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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 | 0.130 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.031 | 0.059 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".