Mechanical ventilation and clinical practice heterogeneity in intensive care units: a multicenter case-vignette study
Bibliographic record
Abstract
BACKGROUND: Observational studies on mechanical ventilation (MV) show practice variations across ICUs. We sought to determine, with a case-vignette study, the heterogeneity of processes of care in ICUs focusing on mechanical ventilation procedures, and whether organizational patterns or physician characteristics influence practice variations. METHODS: We conducted a cross-sectional multicenter study using the case-vignette methodology. Descriptive analyses were calculated for each organizational pattern and respondent characteristics. An Index of Qualitative Variation (IQV, from 0, no heterogeneity, to a maximum of 1) was calculated. RESULTS: Forty ICUs from France (N = 33) and Switzerland (N = 7) participated; 396 physicians answered our case-vignettes. There was major heterogeneity of management processes related to MV within and across centers (mean IQV per center 0.51, SD 0.09). We observed the lowest variability (mean IQV per question < 0.4) for questions related to intubation procedure, ventilation of acute respiratory distress syndrome and the use of the semirecumbent position. We observed a high variability (mean IQV per question > 0.6) for questions related to management of endotracheal tube or suctioning, management of sedation and analgesia, and respect of autonomy. Heterogeneity was independent of respondent characteristics and of the presence of written procedures. There was a correlation between the processes associated with the highest variability (mean IQV per question > 0.6) and the annual volume of ICU admission (r = 0.32 (0.01 to 0.58)) and MV (r = 0.38 (0.07 to 0.63)). Within ICUs there was a large heterogeneity regarding knowledge of a local written procedure. CONCLUSIONS: Large clinical practice variations were found among ICUs. High volume centers were more likely to have heterogeneous practices. The presence of a local written procedure or respondent characteristics did not influence practice variation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".