Describing out-of-hospital cardiac arrest to improve recognition
Bibliographic record
Abstract
Introductions Out-of-hospital cardiac arrests (OOHCA) often go untreated by lay-rescuers. One barrier to response is poor recognition. This study’s purpose is to describe OOHCA using publicly available videos. Materials and Methods 26 of the internet’s most popular video-hosting and social media platforms were consecutively searched in English and Chinese August 3rd to January 20th, 2018 until each site returned 100 consecutive unrelated videos. Video inclusion required: i) medium to high definition video quality (>360p and >10 frames per second), ii) that cardiac arrest be confirmed from two sources (i.e. news, social media, etc.), iii) 100% reviewer agreement on pre-arrest and post-arrest signs, and iv) arrest have non-traumatic etiology. Results 821 videos were identified; 165 videos met inclusion criteria and underwent content analysis. 68, victims (41%) exhibited pre-arrest signs: 34 (21%) had unsteady gait; 42 (26%) touched their head or neck; and 33 (20%) hip-flexed or squatted prior to collapse. After collapse, 97 (59%) exhibited signs of life such as agonal breathing (71, 43%) or posturing/convulsions (39, 24%). Most common lay-responses were: 38 (28%) victims were shaken, 28 (17%) received chest compression(s), 18 (11%) had their head held, 17 (10%) were unsuccessfully lifted to a standing position, 9 (5%) had their legs raised, and 5 (3%) had an AED applied. Discussion Analysis suggests three times as many victims of cardiac arrest show some signs of life compared to no signs of life, and that bystander response is poor. Publicly available videos offer rich examples of what OOCHA collapse and resuscitation look like and could inform training.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".