Cardiopulmonary Resuscitation Case Registration Protocol; a Centralized and Prospective Registry in Kermanshah, Iran
Bibliographic record
Abstract
Background: Cardiopulmonary resuscitation (CPR) is a life-saving procedure that helps maintain blood circulation and breathing in individuals experiencing cardiopulmonary arrest. Various factors with different effects influence CPR outcomes. This study describes the protocol of a CPR Case Registry based in Kermanshah, Iran. Methods: The Kermanshah CPR registry’s methodology is a combination of several existing registration methods, including those of the US, Europe, Canada, Australia, and Japan. All patients presenting to the hospital with cardiopulmonary arrest, including arrests within hospital inpatient wards, are included in the registry. Data are collected electronically and instantly through the checklist designed in the medical records system every 24 hour. The success rate of resuscitation cases and its influencing factors are documented according to the predesigned checklist compiled from previous registries. This registry was established in June 2019, and more than 1500 participants are examined annually. The results of this study will be compared with those of other similar studies. Discussion: To this date, 1234 cases (59% male) have been recorded in the registry with 34.7% successful CRP attempts. The registry of cardiopulmonary resuscitation cases provides data regarding the influencing factors of CPR outcomes.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".