Determinants of diaphragm thickening fraction during mechanical ventilation: an ancillary study of a randomised trial
Bibliographic record
Abstract
Ultrasonography of the diaphragm is the subject of a growing interest in the intensive care unit (ICU) setting [1–6]. Observing the diaphragm in its zone of apposition allows measurement of its thickness and computation of its thickening fraction (TFdi), which depends on diaphragmatic activity [3] and reflects the diaphragm work of breathing [1]. A recent study showed that the TFdi correlated well with the endotracheal pressure variation generated by phrenic stimulation [6]. This index was also proposed for clinical evaluation of diaphragm weakness to detect ventilator-induced diaphragmatic dysfunction (VIDD) and predict difficult weaning [3, 4]. However, it remains unclear whether increased thickening in this setting only reflects a better intrinsic diaphragmatic strength, or if it also suggests enhanced work of breathing in response to increased cardiorespiratory workload. Furthermore, some authors suggested that VIDD could be thought as the “respiratory” manifestation of a global neuromuscular weakness [4, 7], but its relationship with ICU-acquired limb weakness is not straightforward [5]. The present study had a dual objective: first, to explore the correlation between ICU-acquired limb weakness (as assessed by the Medical Research Council (MRC) score) and diaphragm thickening (as assessed by TFdi); second, to assess the association of clinical variables with TFdi during mechanical ventilation. Diaphragm thickening does not correlate with ICUAW; it is influenced by cardiopulmonary load and residual sedation
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".