In Reply
Bibliographic record
Abstract
We thank Dr. Treschan et al. for their interest in our article1 and appreciate the correction regarding the methodology used in their study.2 Indeed, they indicate that a single recruitment maneuver before extubation does not mitigate the potentially deleterious effects of low tidal volumes in the surgical population.Their letter refers to important data on the utility and safety of a ventilation strategy in the operating room, published since our commentary went to press.3 The PROtective Ventilation using HIgh versus LOw positive end-expiratory pressure Trial, which randomized 900 patients to low or high positive-end expiratory pressure (PEEP), demonstrated no reduction in postoperative complications from higher PEEP3; in contrast, higher PEEP was associated with higher rates of intraoperative hypotension and vasopressor and fluid administration. However, it has been noted4 that the levels of PEEP used in this study3 were high (mean of 12 cm H2O). What is clear at this point is that (1) the optimum level of PEEP for intraabdominal surgery remains unknown, and (2) the addition of PEEP, while often beneficial in the critically ill, might cause harm during routine surgery.An additional recent study also gives pause to the early adoption of low tidal volume ventilation in the operating room.5 This large retrospective study of more than 29,000 patients undergoing general anesthesia in a U.S. center suggested that the relation between tidal volume and mortality at 30 days was complex (lowest mortality with 8 to 10 ml/kg; higher above or below this range). Although representing an association (and not proof of cause and effect), it does support the need for more data before widespread adoption of low tidal volume during surgery.The authors declare 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 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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.034 | 0.028 |
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".