Lachgas im Kreißsaal: Erfahrungen aus einem großen Nordamerikanischen Zentrum Laughing gas in labour & delivery: experience from a big North American center
Bibliographic record
Abstract
Introduction Nitrous oxide (NO), an inhaled anesthetic gas commonly known as laughing gas, is an inexpensive and effective form of pain relief in labour. Benefits include its rapid onset of action and quick elimination through the maternal respiratory system, lack of effect on uterine contractility and possible use in all stages of labour. However, there are many misconceptions regarding its use, e.g. that it can prolong labour and is unsafe for patients. Therefore, NO is infrequently offered or used for labour analgesia. The goal of our study was to audit the current use of NO in the Labour & Delivery Unit (L&D) at Mount Sinai Hospital in Toronto, Canada (MSH). Methods We conducted a retrospective audit for parturients admitted to L&D at MSH for induction of or in active labour between 01.07.2019 and 31.12.2019 and determined incidence of analgesics used, in general and depending on mode of delivery. We also created and distributed a survey in April 2022, in order to investigate the perceptions of labour care providers. Results The results from the audit showed that from 696 patients admitted to MSH, 2% used NO, 84% neuraxial, 6% different and 8% no analgesia. 144 labour care providers answered the survey. 11.3 % strongly agreed, 54.8 % agreed, 18.6 % neither agreed nor disagreed and 15.3 % disagreed that NO is an effective form of analgesia in labour. Conclusion Neuraxial analgesia is used more commonly than NO in labour, which necessitates increased education regarding the appropriate use of NO in L&D. Publication History Article published online: 11 October 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".