Repeatability of RRate measurements in children during triage in two Ugandan hospitals
Bibliographic record
Abstract
Pneumonia is the leading cause of death in children globally. In low- and middle-income countries (LMICs) pneumonia diagnosis relies on accurate assessment of respiratory rate, which can be unreliable when completed by nurses with less-advanced training. To inform more accurate measurements, we investigate the repeatability of the RRate app used by nurses in Ugandan district hospitals. This secondary analysis included 3,679 children aged 0-5 years. The dataset had two sequential measurements of respiratory rate collected by 14 nurses using the RRate app. We measured agreement between respiratory rate observations while indicating observations' clustering around WHO fast-breathing thresholds. WHO thresholds are 60 breaths per minute (bpm) for under two months (Age-1), 50 bpm for two to 12 months (Age-2), and 40 bpm for 12.1 to 60 months (Age-3). We assessed the repeatability of the paired measurements per user through the Intraclass Correlation Coefficient (ICC) and calculated an overall ICC value. The respiratory rate measurement took less than 15 seconds for 7,277 (98.9%) of the measurements. Despite respiratory rates clustering around WHO thresholds, breathing classification based on the thresholds (Fast vs normal) was altered between sequential measurements in only 12.6% of children. The mean (SD) respiratory rate by age group was 60 (13.1) bpm for Age-1, 49 (11.9) bpm for Age-2, and 38 (10.1) for Age-3, and the bias (Limits of Agreements) were 0.3 (-10.8-11.3) bpm, 0.4 (-8.5-9.3) bpm, and 0.1 (-6.8, 7.0) bpm for Age-1, Age-2, and Age-3 respectively. The repeatability of the paired respiratory rate measurements was high, with an ICC ≥ 90% for 12 of 14 users and an overall ICC value (95% CI) of 0.95 (0.94-0.95). The RRate measurements were efficient and repeatable. The simplicity, repeatability, and efficiency support its usage in LMICs healthcare facilities, and endorses a more widespread clinical adoption.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".