Influence of traction therapy of the cervical spine on the distribution of harmonics in the voice as well as the intensity and length of phonation
Bibliographic record
Abstract
Background: Contemporary physiotherapeutic techniques and methods could be useful in changing the approach to teaching correct phonation in people with different vocal experience.A physiotherapist can use stimulating techniques, improving the lower rib breathing in a vocalist, using thoracic expansion techniques, diaphragm decompression or manual massage, as well as spinal and rib mobilization.Moreover, many modalities for temporomandibular joint (TMJ) therapy can be useful for working with vocalists.Aims: The aim of the study was to find the relationship between the distribution of harmonics, sound intensity, and phonation length and the use of the cervical spine traction technique in people with different vocal experience. Material and methods:The study in a group of 35 people was carried out at the Physioteam clinic in Wroclaw (Poland).First, the respondent was asked to complete a questionnaire regarding his experience and aspects of vocal education.Then the measure was conducted from the distance of 30 cm from the mouth to the membrane and took a sound sample on C-sound (130 Hz) for men and (260 Hz) for women.After recording the sample, we moved on to traction.Traction was performed on a massage table, in the supine position, by one physiotherapist for all persons from the study.The technique was performed for 2 minutes in each of the 3 positions: the head is slightly extended and the neck is flexed, the head and neck in right-sided rotation, and the head and neck in left-sided rotation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".