Predictive value of positional change in vital capacity to identify diaphragm dysfunction
Bibliographic record
Abstract
Rationale: Sitting-to-supine fall in vital capacity (∆VC) can be used to help identify diaphragm dysfunction (DD), but its optimal predictive threshold value is uncertain. Our aim was to evaluate the diagnostic performance of ∆VC in identifying the presence of unilateral or bilateral DD. Methods: Patients referred to the diaphragm dysfunction clinic of our center (2017-2018) were included. All subjects had lung function testing (including measurement of ∆VC) and an ultrasound assessment of diaphragm thickening fraction (TFdi). Unilateral DD was defined as a single hemidiaphragm with TFdi ≤30% and bilateral DD as a mean TFdi value of both hemidiaphragms ≤30%. Clinical and physiological characteristics were compared across groups, and sensitivity/specificity analyses of ∆VC to identify DD were performed. Results: 84 patients were included (31 unilateral DD, 17 bilateral DD and 36 without significant DD). DD groups had similar age, gender and BMI (all p>0.05), but patients with bilateral DD had lower FVC, FEV1, MIP, TLC, ∆VC and more frequent orthopnea than patients with unilateral DD (all p<0.05). There was a significant correlation between TFdi and ∆VC (rho=-0.56, p<0.001). The optimal ∆VC value to identify bilateral DD was ≤-14% [AUC 0.97 (95%CI 0.93-1.00), p<0.001, with sensitivity and specificity of 100% and 86%, respectively]. No single threshold of ∆VC could accurately predict unilateral DD [AUC 0.57 (95%CI 0.43-0.71), p=0.35]. Conclusion: ∆VC performs poorly in identifying patients with unilateral DD. However, a ∆VC value ≤-14% is strongly associated with the presence of bilateral DD. These findings should be taken into account when using ∆VC in the evaluation of patients with suspected DD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".