Reliability and Validity of Maximal Respiratory Pressures
Bibliographic record
Abstract
BACKGROUND: Maximal respiratory pressure is used to assess the inspiratory and expiratory muscles strength by using maximal inspiratory pressure (P Imax ) and maximal expiratory pressure (P Emax ). This study aimed to summarize and evaluate the reliability and validity of maximal respiratory pressure measurements. METHODS: This systematic review followed the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) recommendations and was reported by using the PRISMA checklist. Studies published before March 2023 were searched in PubMed and EMBASE databases. RESULTS: A total of 642 studies were identified by using the online search strategy and manual search (602 and 40, respectively). Twenty-three studies were included. The level of evidence for test-retest reliability was moderate for P Imax and P Emax (intraclass correlation coefficient > 0.70 for both), inter-rater reliability was low for P Imax and very low for P Emax (intraclass correlation coefficient > 0.70 for both), and the measurement error was very low for P Imax and P Emax . In addition, concurrent validity presented a high level of evidence for P Imax and P Emax (r > 0.80). CONCLUSIONS: Only concurrent validity of maximal respiratory pressure measured with the manometers evaluated in this review presented a high level of evidence. The quality of clinical studies by using maximal respiratory pressure would be improved if more high-quality studies on measurement properties, by following well established guidelines and the COSMIN initiative, were available.
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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 | 0.100 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".