Determining the effective pre‐oxygenation interval in obstetric patients using high‐flow nasal oxygen and standard flow rate facemask: a biased‐coin up–down sequential allocation trial
Bibliographic record
Abstract
Using biased-coin sequential allocation, we sought to determine the effective time interval in 90% of healthy parturients to achieve a target endpoint end-tidal oxygen of ≥ 90% using standard flow rate facemask and high-flow nasal oxygen. Eighty healthy parturients were randomly assigned to standard facemask (n = 40) or high-flow nasal oxygen (n = 40) groups; half of the parturients in the high-flow nasal oxygen group also used a simple no-flow facemask to minimise air entrainment. The effective time interval for 90% of parturients to achieve the target endpoint for standard facemask was 3.6 min (95%CI 3.3-6.7 min), but could not be estimated for the high-flow nasal oxygen groups with or without an additional simple facemask, as eight minutes was insufficient to achieve the target endpoint for 55% and 92% of parturients, respectively. Furthermore, after three minutes, the target endpoint was reached by 71% in the standard facemask group vs. 0% in the high-flow nasal oxygen groups. After four minutes, the target endpoint was reached by 100% in the standard facemask, 80% in the high-flow nasal oxygen with simple facemask and 67% in the high-flow nasal oxygen groups. Beyond four minutes, there was no improvement in pre-oxygenation success using high-flow nasal oxygen. In conclusion, under the conditions of our study, the effective time interval for 90% of parturients to achieve an end-tidal oxygen ≥ 90% for standard flow rate facemask was estimated to be 3.6 min, but could not be estimated for high-flow nasal oxygen groups even after eight minutes.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".