Guidelines for Managing Neuromuscular Block
Bibliographic record
Abstract
To the Editor Although we agree with each of 4 recent editorials discussing the use of neuromuscular blocking drugs and the monitoring of neuromuscular blockade,1–4 it is the editorial by Kopman,1 suggesting the need for evidenced-based guidelines, that prompts this letter. Consensus-based practice parameters on managing neuromuscular block have been developed by experts appointed by the Czech Society of Anesthesiology and Intensive Care a and we encourage leading anesthesia professional organizations (e.g., American Society of Anesthesiologists and/or European Society of Anaesthesiology) to produce evidence and consensus-based guidelines on how best to monitor and manage the perioperative administration of neuromuscular blocking drugs. We believe that having such guidelines would decrease the number of anesthesiologists ignoring the strong evidence of a link between respiratory complications and residual blockade.5–8 Vladimir Cerny, MD, PhD, FCCM Department of Anesthesia Dalhousie University Halifax Halifax, Nova Scotia, Canada Ivan Herold, MD, PhD Department of Anesthesia and Intensive Care Hospital Mlada Boleslav Mlada Boleslav, Czech Republic Karel Cvachovec, MD, PhD, MBA Department of Anesthesia and Intensive Care Charles University in Prague Faculty Hospital in Motol Prague, Czech Republic Pavel Sevcik, MD, PhD Department of Anesthesia and Intensive Care Faculty of Medicine Masaryk University Brno Faculty Hospital Brno Brno, Czech Republic Milan Adamus, MD, PhD Department of Anesthesia and Intensive Care Faculty of Medicine and Dentistry Palacky University Olomouc Faculty Hospital Olomouc Olomouc, Czech Republic [email protected]
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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 | 0.013 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.016 |
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".