Quality of life and prognosis among survivors of out-of-hospital cardiac arrest
Bibliographic record
Abstract
PURPOSE OF REVIEW: We reviewed the topic of quality of life and prognosis of out-of-hospital cardiac arrest survivors, focusing on more recent developments. RECENT FINDINGS: In 2003 to 2004, published experiences from Canada, the United States, and Europe indicate that the quality of life among out-of-hospital cardiac arrest survivors is acceptable and that prognosis may be improving over time. However, a minority of survivors has substantial neurologic impairment that adversely affects the quality of life and prognosis. Recent investigations indicate that bystander CPR, early defibrillation by nontraditional first responders, and active induction of hypothermia offer potential means to improve functional outcomes that may translate into better quality of life and prognosis. Acute treatment of the underlying etiology and effective secondary prevention also can benefit the out-of-hospital cardiac arrest survivor, although only a portion who could benefit appear to receive these therapies. SUMMARY: The optimal strategy for improving long-term outcomes requires a multifaceted, integrative approach that begins with prehospital care and extends to secondary prevention.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".