Current Status of Neuromuscular Reversal and Monitoring: Posttetanic Neuromonitoring and Other Considerations
Bibliographic record
Abstract
The recent comprehensive review article by Drs. Brull and Kopman1 outlines the challenges and opportunities of the current status of neuromuscular reversal and monitoring. Their superlative and informative review is clearly destined to be a go-to reference on the subject. Importantly, it should serve as a rallying point for advancing future neuromuscular blockade (NMB) and function monitoring.Several aspects of this article do warrant additional comment, however. First, the article deals with many important concepts in NMB monitoring and reversal, including not only perioperative considerations, but issues pertinent to the intensive care unit (ICU) where residual neuromuscular blockade, and associated patient awareness, has occasionally been reported.2 Given that the article will rightly take its place as a definitive article on the subject, and as an advocate for postpublication peer-review, I was curious as to why the section discussing awareness from residual paralysis in the ICU included a reference to an article on hypothermia in the ICU (that does not actually mention awareness at all).3 That minor irregularity aside, the excellent text, tables, and figures make for an easy to understand description of all the important concepts in NMB monitoring.A second issue that was particularly interesting was in the discussion of posttetanic count (PTC) as it pertains to posttetanic facilitation. Although the important information the authors provided was accurate, it incompletely addressed an often-misunderstood PTC concept—that is, the time period following a tetanic stimulus that the neuromuscular junction is affected and that subsequent train-of-four (TOF) monitoring might be impaired. Indeed, Hakim et al.4 recently dispelled the common misconception that PTC impairs the NMB for a protracted period of time, showing that TOF responses are reliable as early as one minute after a PTC. I think it is worthwhile bringing this to the readers’ attention, particularly in a definitive and comprehensive article.Lastly, both Brull and Kopman, as well as the accompanying editorial by Naguib and Johnson,5 highlight the importance of moving forward the “state of the art” of NMB monitoring. Importantly, the editorial highlights the American Society of Anesthesiologists’ significant gap in providing guidance on neuromuscular blockade monitoring, particularly when compared with other similar anesthesia societies.6,7 Articles such as this one from Brull and Kopman will, we can hope, encourage the American Society of Anesthesiologists to take a more progressive stance on the subject and advocate for the use of NMB monitoring whenever neuromuscular blocking drugs are used.The author declares no competing interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".