Wheezes, crackles, rhonchi: Agreement among members of the ERS task force on lung sounds
Bibliographic record
Abstract
Background The ERS Task Force on lung sounds was set up in 2012 to establish a repository of audiovisual recordings of lung auscultation for the standardization of nomenclature. We report agreement among the six members of the Task Force on the first 16 recordings. Methods Various adventitious sounds in recordings of lung sounds of 15 seconds duration from 10 children and 6 adults were classified according to recommended English language nomenclature 1 plus the category of rhonchi. Identification by respiratory phase thus offered 10 non-exclusive choices. Kappa statistics was used on pairs of raters to calculate mean Kappa per sound. Results On average there were 2.1 adventitious sounds per rater and patient (range 0.33-3.5). Complete agreement (Kappa=1) was found for 3/10 sounds in 3 of the 15 pairs of raters (fine expiratory crackles and coarse inspiratory and expiratory crackles). Mean Kappa for these were 0.21, 0.31 and 0.33, respectively (fair agreement). The mean Kappa for all ten sounds was 0.20 There was better agreement when merging the inspiratory and expiratory sounds, with a mean Kappa of 0.29. When merging fine and coarse crackles in the analysis and also high-pitched wheezes, low-pitch-wheezes and rhonchi, while still distinguishing between inspiratory and expiratory sounds, the mean Kappa was 0.45 (moderate). Conclusion Only fair interrater agreement was found when 10 different sounds were registered in 16 cases with multiple adventitious sounds. Better agreement was obtained when similar sounds were combined. 1 ACCP-ATS Joint Committee on Pulmonary Nomenclature. Chest 1975; 67:583-93.
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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 | 0.162 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".