Do all ischemic stroke subtypes benefit from organized inpatient stroke care?
Bibliographic record
Abstract
BACKGROUND: There are limited data on the effectiveness of organized stroke care in different ischemic stroke subtypes in the real-world setting. We analyzed the effect of organized stroke care in all stroke subtypes in a longitudinal cohort study using data from the Registry of the Canadian Stroke Network. METHODS: Between July 2003 and September 2007, there were 6,223 consecutive patients with ischemic stroke subtype information by Trial of Org 10172 in Acute Stroke Treatment criteria. Subtypes were categorized as large artery atherosclerotic disease, lacunar, cardioembolic, or other. The amount of organized stroke care was quantified using the previously published organized care index (OCI), graded 0-3 based on the presence or absence of occupational therapy or physiotherapy, stroke team assessment, and admission to a stroke unit. RESULTS: Mortality at 30 days was associated with both stroke subtype and OCI. Higher OCI (defined as score 2-3 compared to 0-1) was strongly associated with lower odds of 30-day mortality in each ischemic stroke subtype (adjusted odds ratio estimates ranged from 0.16 to 0.43, p < 0.001, controlling for age, gender, stroke severity, and medical comorbidities by logistic regression). These estimates were essentially unchanged after excluding patients treated with palliative care. Numbers needed to treat, to prevent 1 death at 30 days, ranged from 4 to 9 across the subtypes. CONCLUSIONS: A strong association between higher OCI and lower 30-day mortality was apparent in each ischemic stroke subtype. These data suggest that organized stroke care should be provided to stroke patients regardless of stroke subtype.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".