Acute heart failure in pre-hospital care. Results from CAREPRE-H registry
Bibliographic record
Abstract
Purpose: To describe the profile, management and early outcome of patients with suspect acute heart failure (AHF) in prehospital setting. Methods: Multicenter prospective registry enrolled consecutive data from the emergency medical system (EMS) during the period of 24 moths. Excluded were patients resuscitated or died before the initial EMS contact. Clinical profile, management and outcome were assessed and predictors of 30-day mortality identified. Results: From the 86584 patients the final diagnosis of AHF was established in 1345 (1.6%) of cases. Their median age was 78 years (57;91), the most common comorbid condition was hypertension (67%), signs of pulmonary oedema were present in 46.4%. Mortality at 30 day after the initial medical contact reached 20.7%. The outcome was more favourable in patients with hypertension (OR 0.51, 95% CI 0.361;0.711) and if physicians did not apply furosemide during the transportation to the hospital (OR 0.61, 95% CI 0.407;0.903). Advanced age (with OR 1.05, 95% CI 1.030;1.061), low oxygen saturation (OR 2.18, 95% CI 1.616;2.934) and the need of vasoactive or inotropic support (OR 3.55, 95%CI 1.710;7.391) were significantly associated with increased mortality. Conclusion: Short-term outcome of AHF patients is unfavourable. Study points out important factors influencing outcome that should be taken into account when managing these patients in prehospital area.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".