Round Table on Malignant Hyperthermia in Physically Active Populations: Meeting Proceedings
Bibliographic record
Abstract
CONTEXT: Recent case reports on malignant hyperthermia (MH)-like syndrome in physically active populations indicate potential associations among MH, exertional heat stroke (EHS), and exertional rhabdomyolysis (ER). However, an expert consensus for clinicians working with these populations is lacking. OBJECTIVE: To provide current expert consensus on the (1) definition of MH; (2) history, etiology, and pathophysiology of MH; (3) epidemiology of MH; (4) association of MH with EHS and ER; (5) identification of an MH-like syndrome; (6) recommendations for acute management of an MH-like syndrome; (7) special considerations for physically active populations; and (8) future directions for research. SETTING: An interassociation task force was formed by experts in athletic training, exercise science, anesthesiology, and emergency medicine. The "Round Table on Malignant Hyperthermia in Physically Active Populations" was convened at the University of Connecticut, Storrs, September 17-18, 2015. CONCLUSIONS: Clinicians should consider an MH-like syndrome when a diagnosis of EHS or ER cannot be fully explained by clinical signs and symptoms presented by a patient or when recurrent episodes of EHS or ER (or both) are unexplained. Further research is required to elucidate the genetic and pathophysiological links among MH, EHS, and ER.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".