Performance evaluation of human cough annotators: optimal metrics and sex differences
Bibliographic record
Abstract
INTRODUCTION: Despite its high prevalence and significance, there is still no widely available method to quantify cough. In order to demonstrate agreement with the current gold standard of human annotation, emerging automated techniques require a robust, reproducible approach to annotation. We describe the extent to which a human annotator of cough sounds (a) agrees with herself (intralabeller or intrarater agreement) and (b) agrees with other independent labellers (interlabeller or inter-rater agreement); we go on to describe significant sex differences in cough sound length and epochs size. MATERIALS AND METHODS: 24 participants wore an audiorecording smartwatch to capture 6-24 hours of continuous audio. A randomly selected sample of the whole audio was labelled twice by an expert annotator and a third time by six trained annotators. We collected 400 hours of audio and analysed 40 hours. The cough counts as well as cough seconds (any 1 s of time containing at least one cough) from different annotators were compared and summary statistics from linear and Bland-Altman analyses were used to quantify intraobserver and interobserver agreement. RESULTS: There was excellent intralabeller (less than two disagreements per hour monitored, Pearson's correlation 0.98) and interlabeller agreement (Pearson's correlation 0.96), using cough seconds as the unit of analysis decreased annotator discrepancies by 50% in comparison to coughs. Within this data set, it was observed that the length of cough sounds and epoch size (number of coughs per bout or attach) differed between women and men. CONCLUSION: Given the decreased interobserver variability in annotation when using cough seconds (vs just coughs) we propose their use for manually annotating cough when assessing of the performance of automatic cough monitoring systems. The differences in cough sound length and epochs size may have important implications for equality in the development of cough monitoring tools. TRIAL REGISTRATION NUMBER: NCT05042063.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".