Oxygen and anesthesia: what lung do we deliver to the post‐operative ward?
Bibliographic record
Abstract
Anesthesia is safe in most patients. However, anesthetics reduce functional residual capacity (FRC) and promote airway closure. Oxygen is breathed during the induction of anesthesia, and increased concentration of oxygen (O(2) ) is given during the surgery to reduce the risk of hypoxemia. However, oxygen is rapidly adsorbed behind closed airways, causing lung collapse (atelectasis) and shunt. Atelectasis may be a locus for infection and may cause pneumonia. Measures to prevent atelectasis and possibly reduce post-operative pulmonary complications are based on moderate use of oxygen and preservation or restoration of FRC. Pre-oxygenation with 100% O(2) causes atelectasis and should be followed by a recruitment maneuver (inflation to an airway pressure of 40 cm H(2) O for 10 s and to higher airway pressures in patients with reduced abdominal compliance (obese and patients with abdominal disorders). Pre-oxygenation with 80% O(2) may be sufficient in most patients with no anticipated difficulty in managing the airway, but time to hypoxemia during apnea decreases from mean 7 to 5 min. An alternative, possibly challenging, procedure is induction of anesthesia with continuous positive airway pressure/positive end-expiratory pressure to prevent fall in FRC enabling use of 100% O(2) . A continuous PEEP of 7-10 cm H(2) O may not necessarily improve oxygenation but should keep the lung open until the end of anesthesia. Inspired oxygen concentration of 30-40%, or even less, should suffice if the lung is kept open. The goal of the anesthetic regime should be to deliver a patient with no atelectasis to the post-operative ward and to keep the lung open.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".