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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".