Reference values for maximal dynamic inspiratory pressure in a healthy population
Bibliographic record
Abstract
As different methods to assess inspiratory muscle function are available for clinical and research applications, studies are required to provide reference values in a healthy population. To establish, from a random Brazilian adult population, a predictive equation for maximal dynamic inspiratory pressure (s-index)(POWERBreathe®). We prospectively evaluated 107 healthy, sedentary, non-smoking and non-obese subjects, from 18 to 80 years old in city of São Paulo. S-index from residual volume to total lung capacity was obtained after at least 8 reproducible maximal maneuvers. To minimize learning effect, test was repeated 30 minutes after rest. Gender-specific prediction equations were developed by multiple regression analysis with s-index as dependent variable, and age, height, weight, and physical fitness as independent ones. To select best prediction model, coefficient of determination (R2) and adjusted R2 was considered, as well as visual scatter plots, distribution of residuals, and multicollinearity. As a dynamic evaluation, s-index showed a strong correlation with FEV1 and FVC (r=0,68 and 0,75; p<0,01, respectively). As well as height, weight and age had significant correlation (r=0,62, 0,46 and -0,38; p<0,01, respectively), however only gender, age and height remained in all final models in a multiple regression approach, explaining 57% of variation in observed values [(height-cm) * 0,716 – (0,348 * age-years + 19)] for male and [(height-cm) * 0,716 – (0,348 * age-years + 41) for female. To our knowledge, that is the first s-index reference equation based on a healthy population considering biological, racial, ethnic and geographical variability.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".