Diagnostic performance of the basic and advanced life support termination of resuscitation rules: A systematic review and diagnostic meta-analysis
Bibliographic record
Abstract
AIM: To minimize termination of resuscitation (TOR) in potential survivors, the desired positive predictive value (PPV) for mortality and specificity of universal TOR-rules are ≥99%. In lack of a quantitative summary of the collective evidence, we performed a diagnostic meta-analysis to provide an overall estimate of the performance of the basic and advanced life support (BLS and ALS) termination rules. DATA SOURCES: We searched PubMed/EMBASE/Web-of-Science/CINAHL and Cochrane (until September 2019) for studies on either or both TOR-rules in non-traumatic, adult cardiac arrest. PRISMA-DTA-guidelines were followed. RESULTS: There were 19 studies: 16 reported on the BLS-rule (205.073 patients, TOR-advice in 57%), 11 on the ALS-rule (161.850 patients, TOR-advice in 24%). Pooled specificities were 0.95 (0.89-0.98) and 0.98 (0.95-1.00) respectively, with a PPV of 0.99 (0.99-1.00) and 1.00 (0.99-1.00). Specificities were significantly lower in non-Western than Western regions: 0.84 (0.73-0.92) vs. 0.99 (0.97-0.99), p < 0.001 for the BLS rule. For the ALS-rule, specificities were 0.94 (0.87-0.97) vs. 1.00 (0.99-1.00), p < 0.001. For non-Western regions, 16 (BLS) or 6 (ALS) out of 100 potential survivors met the TOR-criteria. Meta-regression demonstrated decreasing performance in settings with lower rates of in-field shocks. CONCLUSIONS: Despite an overall high PPV, this meta-analysis highlights a clinically important variation in diagnostic performance of the BLS and ALS TOR-rules. Lower specificity and PPV were seen in non-Western regions, and populations with lower rates of in-field defibrillation. Improved insight in the varying diagnostic performance is highly needed, and local validation of the rules is warranted to prevent in-field termination of potential survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".