Prevalence and impact of inspiratory muscles dysfunction at ICU discharge in patients admitted for acute respiratory failure: Preliminary results of a prospective study
Bibliographic record
Abstract
Critical illness polyneuromyopathy predicts poor outcome. This is in part related to diaphragmatic dysfunction. Prevalence and impact of inspiratory muscle dysfunction in patients who have survived an episode of acute hypercapnic respiratory failure (AHRF) in the ICU is not known. Sniff nasal pressure (Pnas) was measured at day 7 after ICU discharge in 40 consecutive patients surviving an episode of AHRF. Inspiratory muscle dysfunction was defined as Pnas < 5th percentile. Demographic variables, lung physiology, SAPSII and early hospital, RICU and ICU readmission ( 26 patients (65%) were diagnosed with COPD and 22 patients (55%) with obesity. Median Pnas value was 46.0 cmH20 [IQR: 39.5; 61.5]. 27 patients (69%) had inspiratory muscle dysfunction with a median Pnas value of 44 cmH20 [IQR: 35.5; 46], whereas only 12 patients (31%) had a normal inspiratory muscle function with a median Pnas value of 66 cmH20 [IQR: 57.5; 68.5]. Higher FEV1 was associated with normal inspiratory muscle strength (OR 0.95, [CI95: 0.89-0.98], p=0.009) . FEV1 and BMI were positively correlated with Pnas (linear coefficient regression, 0.30, [CI95: 0.07-0.53], p=0.013, respectively linear coefficient regression, 0.55; [CI95: 0.12-0.98], p=0.016). Inspiratory muscle dysfunction was associated with early ward, RICU and ICU readmission (log rank test, p= 0.021). Inspiratory muscle dysfunction is frequent after an episode of AHRF in the ICU and is associated with early hospital, RICU and ICU readmission. Higher BMI and FEV1 are correlated with improved inspiratory muscle strength.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".