Malignant Hyperthermia
Bibliographic record
Abstract
OBJECTIVES: A narrative expert review aiming to summarize the clinical epidemiology and management of critically ill patients with malignant hyperthermia (MH). DATA SOURCES: Medline searches were conducted to identify relevant articles describing the epidemiology, pathophysiology, and management of MH. Guidelines from key MH organizations were also incorporated into this review. STUDY SELECTION: Relevant studies regarding MH in both ICU and perioperative settings were reviewed. DATA EXTRACTION: Data from relevant studies were summarized and qualitatively assessed. DATA SYNTHESIS: MH is a severe reaction triggered by inhalational volatile anesthetics and succinylcholine in genetically susceptible patients. The condition is characterized by an early onset (min to hr) rise in temperature, hypercarbia, and muscular rigidity following exposure to triggering medications with potential complications of coagulopathy, rhabdomyolysis, and acute kidney injury. Acute management necessitates a coordinated multidisciplinary team approach with specific management using dantrolene, active cooling, and hyperventilation. A suspected MH reaction has important implications for future anesthetic exposure for both the patient and their family. All suspected reactions should be followed up at a specialized MH testing center using muscle contracture and genetic testing. CONCLUSIONS: Increasing use of inhalational anesthetics in the ICU underscores the need for enhanced education on the diagnosis and management of MH to ensure optimal patient sedation care and safety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.007 | 0.001 |
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".