Bibliographic record
Abstract
In Canada there are in excess of 40,000 annual cardiac arrests. Unfortunately, survival remains low following both out-of-hospital and in-hospital cardiac arrest, and many premature deaths are believed to be preventable. Studies have shown that high-quality chest compressions are key to survival, and the American Heart Association has summarized the need for: 1) adequate compression depth 2) adequate compression rate 3) avoiding leaning 4) minimizing interruptions 5) and minimizing chest rise. However, both laypersons and professionals are failing to reliably achieve these recommendations. Several devices (which provide real-time visual and audio feedback) have been developed with the goal of improving performance. Voice advisory manikins and motion capture technology utilize accelerometer technology and infrared sensors. Portable devices- including the CPREzyTM, PocketCPRTM, and CPRmeterTM- use accelerometer or pressure sensor technology. A number of defibrillators have been modified to provide real-time feedback. Recently, two applications, iCPR and PocketCPR, have been developed to capitalize on the ubiquity and familiarity of smartphones. These novel devices have shown the potential to improve the quality of chest compressions. What is needed is further research (and development) into how to translate these exciting opportunities into improved survival following cardiac arrest.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".