C ONVERSION R ATES FOR P REHOSPITAL P AROXYSMAL S UPRAVENTRICULAR T ACHYCARDIA (PSVT) WITH THE A DDITION OF A DENOSINE : A B EFORE - AND - AFTER T RIAL
Bibliographic record
Abstract
OBJECTIVE: To determine whether the prehospital administration of adenosine to adults with stable and unstable paroxysmal supraventricular tachycardia (PSVT) influences conversion rate (CR) to sinus rhythm, scene time, use of synchronized electrical cardioversion (SEC), and accuracy of rhythm strip interpretation by paramedics. METHODS: This before-and-after study compared a retrospective control group (CG) prior to the introduction of adenosine with a prospective treatment group (TG) following the addition of adenosine to the PSVT treatment protocol in a large urban advanced life support emergency medical services system. The population represented patients > or = 18 years of age with PSVT diagnosed by the paramedic (defined as spontaneous onset of a regular narrow-complex tachycardia between 140 and 250 beats/minute). RESULTS: The CG comprised 74 calls and the TG 137 calls. The overall CR was higher in the TG (59% vs 32%, p < 0.001). The SEC and spontaneous conversion rates remained unchanged. The proportion of untreated patients with PSVT decreased from 26% CG to 12% TG (p < 0.01). Scene times were longer in the TG (26 vs 19 minutes, p < 0.001). Agreement between paramedic and physician rhythm strip interpretations was fair to moderate (CG kappa 0.43 [95% CI: 0.14, 0.72]; TG kappa 0.37 [95% CI: 0.13, 0.61]). CONCLUSIONS: The introduction of adenosine was associated with a significant increase in the prehospital CR of stable and unstable PSVT, while the SEC and spontaneous conversion rates were similar in each group; however, scene times were longer in the TG and paramedic accuracy in rhythm strip interpretation remained fair to moderate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".