Abstract 179: Bending the Curve: Are We Labelling More Low-risk People as TIA or Has Modern Medical Management of TIA Reduced Stroke Recurrence Over Time?
Bibliographic record
Abstract
Background: Management of transient ischemic attacks (TIA) in specialized stroke centres and prevention clinics have been shown to decrease stroke recurrence and mortality. The impact of these services on a population level are unknown. We aimed to analyze whether modern medical management of TIA has decreased the rate of stroke recurrence and mortality over time in Ontario. Methods: Administrative data from the Canadian Institute for Health Information’s Discharge Abstract Database (DAD) and National Ambulatory Care Reporting System (NACRS) database 2003-2015 were analyzed and rates of stroke recurrence and mortality were calculated. Results: From 2003 to 2015 in Ontario, there were an increasing number of discharges from emergency departments (ED) and a decreasing number of discharges from hospitals. Linear regressions of stroke recurrence at 24 hours, 48 hours, day 7, 30, 90, 180, and 1 year after a TIA showed significantly faster decline between 2003-2015 (p <0.01). Overall stroke recurrence at 1 year decreased from 5.8% to 2.7% between 2003 and 2015, a rate greater than could be explained just from increasing numbers of people labelled as TIA. From 2003-2015, mortality after ED discharge following a TIA decreased from 1.3% to 0.3% (p<0.001), also a greater decline than expected. Interpretations: There is increasing outpatient management of TIAs in Ontario. The observed decline in stroke recurrence was greater than can be explained by referral bias alone, and the increasingly negative slopes of these lines from 24 hours to 1 year suggest a cumulative benefit of improved TIA management over time rather than just increased labelling of low-risk patients. On a population-level, stroke recurrence and mortality after TIA have declined from 2003 to 2015 with a province-wide system of organized stroke care. In contrast to other studies that have shown reduced stroke recurrence and mortality at specialized centres, this study demonstrates the impact of these programs at a population level.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".