Improving the diagnosis of pediatric pneumonia at village level: Testing the accuracy of smart phone applications measuring respiratory rate and O <sub>2</sub> saturation
Bibliographic record
Abstract
Background: Pneumonia is the commonest killer of children under 5 yrs; most deaths occur in the poorest regions. To help improve diagnosis in low-resource areas, we developed iPod/cell phone applications able to measure respiratory rate (RR) and saturation (SpO 2 ). RR is measured using RRate® software (1) which calculates RR from 5 taps of the touch screen corresponding to 5 breaths. SpO 2 is measured using an MS-2040 circuit board-in-cable attached to the iPod port. For the study, applications were run on an iPod Touch 4. We tested their accuracy against accepted standards. Methods: Two blinded observers, in each of three Indian hospitals, made paired observations on 344 children fulfilling WHO criteria for pneumonia. Observer 1 measured RR by 1 minute auscultation (RR.ausc), plus SpO 2 using Masimo Rad7 (SpO 2 .mas). Observer 2 measured RR and SpO 2 by iPod application (RR.tap and SpO 2 .ipod). Paired results compared by Bland-Altman technique. Results : There were no significant differences between RR.ausc and RR.tap or SpO 2 .mas and SpO 2 .ipod, either within or between institutions (table 1) Table 1 RR.ausc vs. RR.tap bias SpO2.mas vs. SpO2.ipod bias Bangalore 1 -1.3 +/- 6.4 bpm 0.6 +/- 3.0 %sat Chennai 1.3 +/- 4.6 bpm 0.3 +/- 1.1 %sat Bangalore 2 0.0 +/- 2.7 bpm 0.1 +/- 1.1 %sat Pooled data -0.4 +/- 5.2 bpm 0.4 +/- 2.4 %sat Conclusions: SpO 2 and RR can be measured accurately and reproducibly using cell phone technology that is easy to use and teach. This has the potential to improve the diagnosis and management of children with respiratory diseases in under-served areas. Reference: 1. https://itunes.apple.com/ca/app/rrate/id581390517?mt=8.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".