Predictors of Mortality in Verapamil Overdose: Usefulness of Serum Verapamil Concentrations
Bibliographic record
Abstract
Verapamil poisoning may result in life-threatening cardiovascular morbidities and fatalities. To date, prognosticators of mortality have been poorly investigated and the use of serum verapamil concentration for prognosis remains unclear. We aimed to evaluate the ability of usual clinical and laboratory parameters including serum verapamil concentrations measured on admission to predict outcome (survival versus death) in verapamil poisoning. We reviewed the medical records of all intentional and symptomatic verapamil poisonings admitted over 8 years to two medical intensive care units. Clinical and laboratory parameters were measured in 65 patients, and final outcomes of survival or death recorded. A multivariable analysis was conducted to evaluate the prognostic values of recorded parameters. Life-threatening complications of verapamil poisonings included shock (62%), atrioventricular blocks (24%), sinoatrial blocks (20%), acute respiratory distress syndrome (19%) and cardiac arrest (11%) resulting in death (8%). Verapamil concentration measured on intensive care unit admission was the only independent factor associated with mortality (p = 0.01). The optimal verapamil cut-off point was 5.0 μM (100% sensitivity, 91% specificity), which conferred a 2.76-times increase in odds of fatality. In conclusion, cardiovascular monitoring and assessment of organ failure are vital in symptomatic verapamil poisonings. The serum verapamil concentration has excellent prognostic ability for predicting fatality in verapamil overdose.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".