Global and regional assessment of sustained inflation pressure–volume curves in patients with acute respiratory distress syndrome
Bibliographic record
Abstract
Abstract Objective . Static or quasi-static pressure–volume ( P – V ) curves can be used to determine the lung mechanical properties of patients suffering from acute respiratory distress syndrome (ARDS). According to the traditional interpretation, lung recruitment occurs mainly below the lower point of maximum curvature (LPMC) of the inflation P – V curve. Although some studies have questioned this assumption, setting of positive end-expiratory pressure 2 cmH 2 O above the LPMC was part of a ‘lung-protective’ ventilation strategy successfully applied in several clinical trials. The aim of our study was to quantify the amount of unrecruited lung at different clinically relevant points of the P – V curve. Approach . P – V curves and electrical impedance tomography (EIT) data from 30 ARDS patients were analysed. We determined the regional opening pressures for every EIT image pixel and fitted the global P – V curves to five sigmoid model equations to determine the LPMC, inflection point (IP) and upper point of maximal curvature (UPMC). Points of maximal curvature and IP were compared between the models by one-way analysis of variance (ANOVA). The percentages of lung pixels remaining closed (‘unrecruited lung’) at LPMC, IP and UPMC were calculated from the number of lung pixels exhibiting regional opening pressures higher than LPMC, IP and UPMC and were also compared by one-way ANOVA. Main results . As results, we found a high variability of LPMC values among the models, a smaller variability of IP and UPMC values. We found a high percentage of unrecruited lung at LPMC, a small percentage of unrecruited lung at IP and no unrecruited lung at UPMC. Significance . Our results confirm the notion of ongoing lung recruitment at pressure levels above LPMC for all investigated model equations and highlight the importance of a regional assessment of lung recruitment in patients with ARDS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".