Repeatability of Pulse Oximetry Measurements in Children During Triage in Two Ugandan Hospitals
Bibliographic record
Abstract
ABSTRACT Background In low- and middle-income countries, health workers use pulse oximeters for intermittent spot measurements of SpO 2 . However, the accuracy and reliability when used for spot measurements has not been determined. We evaluated the repeatability of spot measurements, and the ideal observation time of measurement to guide recommendations during spot check measurements. Methods Two one-minute measurements were done for the 3,903 subjects enrolled in the study, collecting 1Hz SpO 2 and signal quality index (SQI) data. The repeatability between the two measurements was assessed using an intraclass correlation coefficient (ICC), calculated using a median of all seconds of non-zero SpO 2 values for each recording (any quality, Q1), and again with a quality filter only using seconds with SQI ≥ 90% (good quality, Q2). The ICC was also calculated for both these conditions using subsets of the minute, in increasing increments of 5 seconds, up to the whole minute. Lastly, the whole minute ICC was calculated with good quality (Q2), including only records where both measurements had a mean SQI > 70% (Q3). Findings The repeatability ICC with condition Q1 was 0.591 (95% confidence interval (CI) = 0.570, 0.611). Using only the first 5 seconds of each measurement reduced the repeatability to 0.200 (95% CI = 0.169, 0.230). Filtering with Q2, the whole minute ICC was 0.855 (95% CI = 0.847, 0.864). The ICC did not improve beyond the first 35 seconds. For Q3, the repeatability rose to 0.908 (95% CI = 0.901, 0.914). Conclusions Training guidelines must emphasize the importance of signal quality and duration of measurement, targeting a minimum of 35 seconds of adequate-quality, stable data. In addition, the design of new devices should incorporate user prompts and force quality checks to encourage more accurate pulse oximetry measurement. Trial Registration Clinical Trials.gov Identifier: NCT04304235 , Registered 11 March 2020.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".