Reducing âFailure-to-Rescueâ Events through Enhanced Critical Care Response Teams
Bibliographic record
Abstract
Failure to recognize and respond to changes in a patient’s condition is a limitation in the effective utilization of Medical Emergency Teams (METs). \nA system that uses smartphone technology to facilitate vital signs collection at bedside has been developed. The alerts engine, based upon Mount Sinai Hospital’s (MSH) MET calling criteria, can automatically alert the MET of patients exhibiting abnormal vital signs.\nThe system, without automated alerting, was piloted at MSH. Sensitivity and specificity calculations revealed that the MSH algorithm had a lower sensitivity and specificity than the Cuthbertson or the Modified Early Warning Score algorithms. This suggests that the MSH algorithm, compared to the others, was poor at identifying patients that did and did not require a MET consultation. Furthermore, the low positive predictive value suggests that the majority of alerts were not associated with a MET call. Therefore, the MSH algorithm is not recommended for the automated system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.012 | 0.002 |
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".