Factors Influencing Outcome and Treatment Effect in PROACT II
Bibliographic record
Abstract
17 PROACT II was a randomized controlled trial of intra-arterial thrombolysis with recombinant prourokinase (r-proUK) plus low-dose heparin vs. low-dose heparin alone. In the primary outcome analysis, 40% of patients treated with r-proUK and 25% of control patients had a good neurological recovery at 90 days defined by a modified Rankin score≤2 (p=.043). Objective: To identify prognostic factors for the primary outcome measure and determine whether the treatment effect of r-proUK was consistent across subgroups stratified according to risk. Methods: Twenty-seven baseline categorical variables and 11 quantitative variables were selected. Stepwise logistic regression was used to identify important prognostic factors and to create a score which subdivided the study group into risk quartiles. Treatment effect was calculated as an odds ratio (OR) within each quartile and compared with a Breslow-Day test. Results: Three baseline variables were identified as strongly influencing the probability of a good outcome; Age ≤ 68 (p<0.0001, OR=4.3), CT hypodensity ≤ 5.25ml (p=0.038, OR=2.1), NIHSS score: (p= 0.0005, OR(11–20)=0.36, OR(>20)=0.085, relative to NIHSS≤10). The effect of treatment in the risk quartiles is summarized in the Table. Comparison of the size of the treatment effect over risk quartiles was non-significant p=0.91. Conclusion: Despite stratification of patients based on important prognostic variables there was no evidence of a difference in treatment effect across risk categories. All PROACT eligible patients stand to benefit from early intra-arterial r-proUK treatment.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".