Pre-arrest prediction of survival following in-hospital cardiac arrest: A systematic review of diagnostic test accuracy studies
Bibliographic record
Abstract
AIM: To evaluate the test accuracy of pre-arrest clinical decision tools for in-hospital cardiac arrest survival outcomes. METHODS: We searched Medline, Embase, and Cochrane Library from inception through January 2022 for randomized and non-randomized studies. We used the Quality Assessment of Diagnostic Accuracy Studies framework to evaluate risk of bias, and Grading of Recommendations Assessment, Development and Evaluation methodology to evaluate certainty of evidence. We report sensitivity, specificity, positive predictive outcome, and negative predictive outcome for prediction of survival outcomes. PROSPERO CRD42021268005. RESULTS: We searched 2517 studies and included 23 studies using 13 different scores: 12 studies investigating 8 different scores assessing survival outcomes and 11 studies using 5 different scores to predict neurological outcomes. All were historical cohorts/ case control designs including adults only. Test accuracy for each score varied greatly. Across the 12 studies investigating 8 different scores assessing survival to hospital discharge/ 30-day survival, the negative predictive values (NPVs) for the prediction of survival varied from 55.6% to 100%. The GO-FAR score was evaluated in 7 studies with NPVs for survival with cerebral performance category (CPC) 1 ranging from 95.0% to 99.2%. Two scores assessed survival with CPC ≤ 2 and these were not externally validated. Across all prediction scores, certainty of evidence was rated as very low. CONCLUSIONS: We identified very low certainty evidence across 23 studies for 13 different pre-arrest prediction scores to outcome following IHCA. No score was sufficiently reliable to support its use in clinical practice. We identified no evidence for children.
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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 | 0.054 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".