Monitoring respiratory rate based on tracheal sounds. First experiences
Bibliographic record
Abstract
The objective was to develop a non-invasive method for continuously monitoring respiratory rate (RR) based on tracheal sounds. 25 volunteers and 36 patients with chronic pulmonary diseases were enrolled in a clinical study. Tracheal sounds were acquired using a contact piezoelectric sensor placed on the examinee's throat and analyzed using a combined investigation of the sound envelope and frequency content. RR estimates were compared to reference measurements taken from a pneumotachometer coupled to a face mask worn by the examinee. RR was also manually counted by a respiratory technician. Two types of breathing (mouth and nose) and three different positions were studied (fowler, semi-fowler and supine). RR estimated in volunteers had a success rate (SR) of 96%, a correlation coefficient (r) of 0.99 and a standard error of the estimate (SEE) of 0.56. The RR estimated in patients was comparable or slightly better (SR = 85%, r = 0.93 and SEE = 1.49) than those obtained by manual count (SR = 82%, r = 0.91, SEE = 1.58), which is the method widely used in clinical settings. No significant difference in the capacity to estimate RR was found related to posture and breathing type, making this method useful for continuous monitoring.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".