Breathing pattern and kinematics in normal subjects during speech, singing and loud whispering
Bibliographic record
Abstract
AIMS: We used for the first time a non-invasive optoelectronic plethysmography to assess breathing movements and to provide a quantitative description of chest wall kinematics during phonation. METHODS: Volumes of different chest wall compartments (abdomen and lung apposed to rib cage and abdomen) were assessed using optoelectronic plethysmography in 16 normal Italians (eight men) during reading, singing and high-effort whispering (HW). RESULTS: During phonation the breathing pattern was different from quiet breathing and exercise. (1) During phonation, tidal volume and expiratory time increased while inspiratory time decreased. The expiratory volume changes and flows during HW were considerably greater than during vocalization. During HW, the overall end-expiratory thoracic volume significantly decreased as a result of decreased volume of all compartments and essentially impinged on the maximal expiratory flow-volume curve. (2) While, as previously shown, during exercise the expired volume is due entirely to the abdomen, during phonation all three chest wall compartments contribute to it. Under all conditions studied breathing was, on average, more costal in females than in males but this was mainly related to different size rather than gender per se. CONCLUSIONS: Physical characteristics have a greater importance than gender in determining breathing pattern and chest wall kinematics during phonation. The activity of the control of expiration during phonation is more complex than during exercise.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".