The PLAN score can predict poor outcomes of intracerebral hemorrhage
Bibliographic record
Abstract
BACKGROUND: For patients hospitalized after acute ischemic stroke (AIS), the preadmission comorbidities, level of consciousness (LOC), age and neurologic deficit (PLAN) score can help to identify those who may have a poor outcome. Implementing the PLAN score in other types of stroke may also have predictive value. Our study aimed to evaluate the PLAN score's prognostic accuracy in predicting 1-year mortality and severe disability after intracerebral hemorrhage (ICH). METHODS: We analyzed data found in the China National Stroke Registry (CNSR) of 2,453 hospitalized patients in 132 urban Chinese hospitals, diagnosed with ICH from September 2007 to August 2008. The outcomes analysis included 30-day mortality, modified Rankin Scale score (mRS) of 5-6 at discharge, and 1-year mortality. Univariate and multivariate analysis was performed, and we calculated consistency statistics (C statistic). We evaluated the PLAN score performance using area under the curve (AUC) calculations. RESULTS: We found that the 30-day mortality was 12.6%, the frequency of a mRS 5-6 at discharge was 20.6%, and 1-year mortality was 21.9%. The PLAN score had good predictive value in 30-day mortality (C statistic, 0.82), death or severe dependence at discharge (0.84), and 1-year mortality (0.82). CONCLUSIONS: In patients hospitalized for ICH, the 30-day mortality, death or severe dependence at discharge and 1-year mortality can be predicted by the PLAN score. Similarly to patients hospitalized after AIS, the PLAN score can help to identify patients likely to have poor outcomes following hospitalization for ICH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".