17th Annual Meeting, Neurocritical Care Society, October 15–18, 2019, Vancouver, Canada
Bibliographic record
Abstract
MethodsThis is a single-center study of patients admitted to a tertiary care center.ML models and SM were trained to predict DCI and functional outcomes with data collected <3 days of admission.DCI status was recorded and functional outcomes at discharge and at 3-months were quantified using the modified Rankin scale (mRS) for neurological disability (dic Concurrently, clinicians prospectively prognosticated 3-month outcomes of patients within 3 days of admission.The performance of ML, SM and clinicians are compared. Results451 subjects were included in the study.DCI status, discharge, and 3-month outcomes were available for 399, 393 and 240 subjects respectively.Prospective clinician (an attending, a fellow and a nurse) prognostication of 3-month outcomes was available for 90 subjects.ML models yielded accurate predictions with the following AUC (area under the receiver operating curve) scores: 0.75 ± 0.07 (95% CI: 0.64 to 0.84) for DCI, 0.85 ± 0.05 (95% CI: 0.75 to 0.92) for discharge outcome, and 0.89 ± 0.03 (95% CI: 0.81 to 0.94) for 3-month outcome.The best ML models performed better than the SMs, improving the AUC by 0.20 (95% CI: -0.02-0.4) for DCI, by 0•07 ± 0.03 (95% CI: -0.0018-0.14)for discharge outcomes, and by 0.14 (95% CI: 0.03 -0.24) for 3-month outcomes and matched physician's performance in predicting 3-month outcomes. ConclusionsML outperform SMs in predicting DCI and match attending physician in predicting 3-month outcomes.ML models has potential to help improve outcomes after SAH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.326 | 0.143 |
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".