Assessing the Quality of Manual Respiratory Rate Measurements using Mobile Devices
Bibliographic record
Abstract
We have designed a mobile device application (RRate), to provide an efficient measurement of respiratory rate with clinically acceptable accuracy. The method is based on analysis of multiple consecutive breath intervals. We investigated in this study the difference in measurement variability between breaths as a representative of recording quality. Respiratory rate of 322 children aged 0 - 12 years at a Ugandan rural health centre were recorded using the RRate mobile application, and compared to respiratory rate recordings obtained from 22 volunteers using the RRate application while observing 10 videos of children breathing in a lab setting. The variability of the recorded breaths (confidence) of both groups follow a similar Weibull distribution. However, we observed a trend towards higher variability in the data obtained in the field (median 89.7% confidence) compared to the data obtained in the laboratory setting (median 92.6% confidence). This suggests that it is more difficult to obtain consistent measurements when assessing patients in a clinical setting, and therefore the confidence in the measured respiratory rate is reduced. The mobile device application provided a respiratory rate value up to 6 times faster than the current practice of one minute counting. The measure of variability between individual measured breaths provided a powerful way to display confidence in a measurement.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".