Days Alive and Out of Hospital as an Outcome Measure in Patients Receiving Hyperacute Stroke Intervention
Bibliographic record
Abstract
Background Patient outcome after stroke is frequently assessed with clinical scales such as the modified Rankin Scale score (mRS). Days alive and out of hospital at 90 days (DAOH‐90), which measures survival, time spent in hospital or rehabilitation settings, readmission and institutionalization, is an objective outcome measure that can be obtained from large administrative data sets without the need for patient contact. We aimed to assess the comparability of DAOH with mRS and its relationship with other prognostic variables after acute stroke reperfusion therapy. Methods and Results Consecutive patients with ischemic stroke treated with intravenous thrombolysis or endovascular thrombectomy were analyzed. DAOH‐90 was calculated from a national minimum data set, a mandatory nationwide administrative database. mRS score at day 90 (mRS‐90) was assessed with in‐person or telephone interviews. The study included 1278 patients with ischemic stroke (714 male, median age 70 [59–79], median National Institutes of Health Stroke Scale score 14 [9–20]). Median DAOH‐90 was 71 [29–84] and median mRS‐90 score was 3 [2–5]. DAOH‐90 was correlated with admission National Institutes of Health Stroke Scale score (Spearman rho −0.44, P <0.001) and Alberta Stroke Program Early CT [Computed Tomography] Score (Spearman rho 0.24, P <0.001). There was a strong association between mRS‐90 and DAOH‐90 (Spearman rho correlation −0.79, P <0.001). Area under receiver operating curve for predicting mRS score >0 was 0.86 (95% CI, 0.84–0.88), mRS score >1 was 0.88 (95% CI, 0.86–0.90) and mRS score >2 was 0.90 (95% CI, 0.89–0.92). Conclusions In patients with stroke treated with reperfusion therapies, DAOH‐90 shows reasonable comparability to the more established outcome measure of mRS‐90. DAOH‐90 can be readily obtained from administrative databases and therefore has the potential to be used in large‐scale clinical trials and comparative effectiveness studies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".