Do All Acute Stroke Patients Receiving tPA Require ICU Admission?
Bibliographic record
Abstract
BACKGROUND: Limited resources warrant investigating models for predicting which stroke tissue-type plasminogen activator (tPA) patients benefit from admission to neurologic intensive care unit (neuroICU). METHODS: This model classifies patients who on day 1 of their ICU admission are predicted to receive one or more of 30 subsequent active life supporting treatments. Two groups of patients were compared: low risk monitor (LRM) (patients who did not receive active treatment (AT) on the first day and whose risk of ever receiving active treatment was ≤ 10%) and AT (patients who received at least one treatment on any day of their ICU admission). RESULTS: Compared to LRM group (21 patients), AT group (59 patients) had similar age (75 ± 13 vs. 72 ± 17, P = 0.4), similar gender (male: 56% vs. 52%, P = 0.8), similar National Institutes of Health stroke scale (NIHSS, 16 ± 9 vs. 14 ± 8, P = 0.4), and higher Acute Physiologic and Chronic Health Evaluation (APACHE) III scores (62 ± 26 vs. 41 ± 15, P = 0.0008). Compared to LRM group, AT group had longer ICU length of stay (4.5 ± 4.4 vs. 2.5 ± 1.3, P = 0.04), higher ICU mortality (22% vs. 4.7% (one patient DNR/hospice); OR: 5.6; 95% CI: 0.7 - 46.0; P = 0.1), and higher hospital mortality (36% vs. 4.7%; OR: 11; 95% CI: 1.4 - 88.0; P = 0.02). CONCLUSION: The outcome of LRM patients with stroke post-tPA suggests that they may not require admission to a formal neuroICU, improving resource use and reducing costs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".