Influence of 4 Interfaces in the Assessment of Maximal Respiratory Pressures
Bibliographic record
Abstract
BACKGROUND: The measurement of maximal respiratory pressure (MRP) is a procedure widely used in clinical practice to evaluate respiratory muscle strength through the maximal inspiratory pressure (P(Imax)) and maximal expiratory pressure (P(Emax)). Its clinical applications include diagnostic procedures and evaluating responses to interventions. However, there is great variability in the equipment and measurement procedures. Understanding the impacts of the characteristics of different interfaces can augment the repeatability of this method and help to establish widely applicable predictive equations. The aim of this study was to evaluate the influence of 4 different interfaces on a subject's capacity to generate MRP and the impact of these interfaces on the repeatability of these measurements. METHODS: Fifty healthy subjects (mean ± SD age 26.36 ± 4.89 y) with normal spirometry were evaluated. MRP was measured by a digital manometer connected to 4 interfaces using different combinations of mouthpieces and tubes. The following variables were analyzed: maximum mean pressure, peak pressure, plateau pressure, and plateau variation. Analysis of variance for repeated measures or a Friedman test was used to compare the 4 interfaces, with P < .008 after Bonferroni adjustment considered significant. RESULTS: There was no significant difference between the 4 interfaces with respect to maximum mean pressure, peak pressure, plateau pressure, or plateau variation for P(Imax) (P ≥ .49) or P(Emax) (P ≥ .11), nor did the number of tests performed to fulfill the criteria of repeatability for P(Imax) (P = .69) or P(Emax) (P = .47) differ among the 4 interfaces. CONCLUSIONS: P(Imax) and P(Emax) values seem not to be influenced by the different interfaces studied, suggesting that patient comfort and availability of interfaces can be considered.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".