Antidote use for cardiac arrest or hemodynamic instability due to cardiac glycoside poisoning: A narrative review
Bibliographic record
Abstract
Introduction: Cardiac glycosides comprise medications such as digoxin and digitoxin, plants, and even certain toad venoms. Intoxication with cardiac glycosides can lead to hemodynamic instability and cardiac arrest. With this narrative review, our objective was to determine if any therapy used in a near-cardiac arrest state due to cardiac glycoside poisoning could improve survival with favourable functional and neurological outcomes. Methods: We searched the Medline, PubMed, EMBASE and Cochrane Library databases up to February 2022 for controlled trials, observational studies, and case reports. We reviewed studies if participants were exposed to a cardiac glycoside, had hemodynamic instability, and an intervention was attempted to reverse the toxicity. The effect of interventions on (1) survival with favourable functional and neurological outcomes and (2) correction of hemodynamic instability was assessed. Results: Of the 2422 studies found, 73 were included for analysis, of which 58 were case reports or series, and 15 were observational cohorts. Most patients were intoxicated with medication (60 individual cases and 11 observational cohorts). Administration of digoxin immune-Fab fragments was associated with improved hemodynamic status and survival in medication patients. Administration of magnesium, cardioversion, and cardiac pacing was associated with favourable outcomes, while administration of atropine, antiarrhythmics, or calcium was not. Conclusion: In patients with hemodynamic instability due to cardiac glycoside intoxication, digoxin immune-Fab fragments should be given, and magnesium administration, cardioversion, and cardiac pacing can reasonably be attempted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".