Costs related to cardiac arrest management: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Each year, about 500,000 people suffer a cardiac arrest (either out-of-hospital or in-hospital) in the USA. Although significant improvements in survival have occurred through the implementation of complex high-quality protocols of care, global costs related to such management are not clearly described. METHODS: We will undertake a systematic review of the published literature on costs related to the acute phase of cardiac arrest management (from collapse to hospital discharge). The search will cover the period 1991 to present, and we will include studies written in English or in French involving patients with cardiac arrest of all ages, settings (in- and out-of-hospital arrest), countries, and etiology (including traumatic). The primary outcome will include estimates of costs related to cardiac arrest patients' management in various categories (e.g., resuscitation process, in-hospital management as well as rehabilitation and long-term care facilities) and perspectives (e.g., hospital, societal, or third-payer perspective). Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and data quality will be assessed by questions adapted from the Drummond economic evaluation checklist. DISCUSSION: This review will provide an estimate of costs related to cardiac arrest management according to the different components of such a management as well as total costs. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register of Systematic Reviews PROSPERO CRD42016046993.
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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 | 0.081 | 0.085 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.019 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.010 |
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".