Lung Ultrasonography for the Assessment of Perioperative Atelectasis: A Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Few diagnostic tools are available to anesthesiologists when confronted with intraoperative hypoxemia. Lung ultrasonography is a safe and accurate bedside imaging modality. The aim of this study was to evaluate the feasibility of lung ultrasonography during the perioperative period and assess its ability to detect intraoperative respiratory complications and oxygenation changes resulting from perioperative atelectasis. METHODS: In this prospective observational pilot study, 30 consecutive patients scheduled for laparoscopic surgery were recruited. Mechanical ventilation was standardized. Lung ultrasonography was performed at 5 predefined time points: before induction of general anesthesia (GA), after induction of GA, after pneumoperitoneum insufflation, on arrival in the recovery room, and before recovery room discharge. For each echographic examination, 12 pulmonary quadrants were imaged. From these, a semiquantitative score, the lung ultrasound (LUS) score, was calculated to assess lung aeration at each time point. RESULTS: Lung ultrasonography was possible in all patients. Changes in the LUS score between the postinduction period and arrival in the recovery room were correlated with changes in oxygenation (Spearman r = -0.43, P = .018). Induction of GA was associated with an increase in the LUS score, which gradually worsened at all time points until recovery room discharge. This increase was significantly worse in the basal and dependent lung zones. Lung ultrasonography helped in the detection of 2 capnothoraces, 1 endobronchial intubation, and 1 episode of subclinical pulmonary edema. CONCLUSIONS: Lung ultrasonography in the perioperative period is feasible, allows tracking of perioperative atelectasis, and facilitates the diagnosis of respiratory complications. The evolution of aeration loss correlates moderately with changes in oxygenation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".