Bibliographic record
Abstract
Background: We sought to develop and validate a simple clinical prediction rule for death and severe disability after acute ischemic stroke that can be used by general clinicians at the time of hospital admission. Methods: We analyzed data from a registry of 9847 patients (4943 in the derivation cohort and 4904 in the validation cohort) hospitalized with acute ischemic stroke and included in the Registry of the Canadian Stroke Network (July 1, 2003, to March 31, 2008; 11 regional stroke centers in Ontario, Canada). Outcome measures were 30-day and 1-year mortality and a modified Rankin score of 5 to 6 at discharge. Results: Overall 30-day mortality was 11.5% (derivation cohort) and 13.5% (validation cohort). In the final multivariate model, we included 9 clinical variables that could be categorized as preadmission comorbidities (5 points for preadmission dependence [1.5], cancer [1.5], congestive heart failure [1.0], and atrial fibrillation [1.0]), level of consciousness (5 points for reduced level of consciousness), age (10 points, 1 point/decade), and neurologic focal deficit (5 points for significant/total weakness of the leg [2], weakness of the arm [2], and aphasia or neglect [1]). Maximum score is 25. In the validation cohort, the PLAN score (derived from preadmission comorbidities, level of consciousness, age, and neurologic deficit) predicted 30-day mortality (C statistic, 0.87), death or severe dependence at discharge (0.88), and 1-year mortality (0.84). The PLAN score also predicted favorable outcome (modified Rankin score, 0-2) at discharge (C statistic, 0.80). Conclusions: The PLAN clinical prediction rule identifies patients who will have a poor outcome after hospitalization for acute ischemic stroke. The score comprises clinical data available at the time of admission and may be determined by nonspecialist clinicians. Additional studies to independently validate the PLAN rule in different populations and settings are required.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.028 |
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".