Correlation of Paramedic Administration of Furosemide with Emergency Physician Diagnosis of Congestive Heart Failure
Bibliographic record
Abstract
Objective Recent literature has questioned the accuracy of paramedic diagnosis of congestive heart failure and appropriateness of administration of diuretics. This study determined the agreement between paramedic administration of furosemide and emergency physician diagnosis of congestive heart failure. Treatments administered in the pre-hospital and emergency departments and adverse events are also described. Methods This retrospective study included patients treated with furosemide by paramedics from November 1, 2006 to June 1, 2008. Paramedic reports were matched with Emergency Department (ED) charts. Emergency physician diagnosis, prehospital and ED treatments, adverse events and mortality were identified. Results Of 94 patients, emergency physician diagnosis was congestive heart failure (CHF) in 60 cases, indicating agreement of 63.8% of paramedic administration of furosemide for this diagnosis. Leading alternate diagnoses were: pneumonia (n = 14); acute coronary syndrome (n = 8); chronic obstructive pulmonary disease (n = 7). The rate of death was higher in patients not diagnosed with congestive heart failure (6/28 vs. 2/58, p=0.017). Eight non-fatal adverse events were identified, all were patients diagnosed with congestive heart failure by emergency physicians. Conclusion Paramedic administration of furosemide demonstrates moderate agreement with physician diagnosis of congestive heart failure. This adds to the evidence that diagnosing the cause of dyspnea in the prehospital setting is difficult, most often confused with pneumonia. Paramedics should be cautious when administering furosemide, as it may be related to increased mortality.
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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 | 0.005 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".