The acoustic cough monitoring and manometric profile of cough and throat clearing
Bibliographic record
Abstract
Cough and throat clearing might be difficult to differentiate when trying to detect them acoustically or manometrically. The aim of this study was to assess the accuracy of acoustic monitoring for detecting cough and throat clearing, and to also determine whether these two symptoms present with different manometric profiles on esophageal pressure topography. Ten asymptomatic volunteers (seven females, mean age 31.1) were trained to simulate cough and throat clearing in a randomized order every 6 minutes during simultaneous acoustic monitoring and high-resolution manometry. The accuracy of automated acoustic analysis and two blinded reviewers were compared. The pattern of the events and the duration of the pressure changes were assessed using the 30 mmHg isobaric contour. There were 50 cough and 50 throat-clearing events according to the protocol. The sensitivity and specificity of automated acoustic analysis was 84% and 50% for cough, while the blinded analysis using sound revealed a sensitivity and specificity of 94% and 92%. The manometric profile of both cough and throat clearing was similar in terms of qualitative findings; however, cough was associated with a greater number of repetitive pressurizations and a more vigorous upper esophageal sphincter contraction compared with throat clearing. The acoustic analysis software has a moderate sensitivity and poor specificity to detect cough. The profile of cough and throat clearing in pressure topography revealed a similar qualitative pattern of pressurization with more vigorous pressure changes and a greater rate of repetitive pressurizations in cough.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".