Tidal changes on CT and progression of ARDS
Bibliographic record
Abstract
BACKGROUND: Uncertain prediction of outcome in acute respiratory distress syndrome (ARDS) impedes individual patient management and clinical trial design. OBJECTIVES: To develop a radiological metric of injurious inflation derived from matched inspiratory and expiratory CT scans, calibrate it in a model of experimental lung injury, and test it in patients with ARDS. METHODS: 73 anaesthetised rats (acid aspiration model) were ventilated (protective or non-protective) for up to 4 hours to generate a spectrum of lung injury. CT was performed (inspiratory and expiratory) at baseline each hour, paired inspiratory and expiratory images were superimposed and voxels tracked in sequential scans. In nine patients with ARDS, paired inspiratory and expiratory CT scans from the first intensive care unit week were analysed. RESULTS: In experimental studies, regions of lung with unstable inflation (ie, partial or reversible airspace filling reflecting local strain) were the areas in which subsequent progression of injury was greatest in terms of progressive infiltrates (R=0.77) and impaired compliance (R=0.67, p<0.01). In patients with ARDS, a threshold fraction of tissue with unstable inflation was apparent: >28% in all patients who died and ≤28% in all who survived, whereas segregation of survivors versus non-survivors was not possible based on oxygenation or lung mechanics. CONCLUSIONS: A single set of superimposed inspiratory-expiratory CT scans may predict progression of lung injury and outcome in ARDS; if these preliminary results are validated, this could facilitate clinical trial recruitment and individualised care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".