SMART O2: Adjusting oxygen to patient needs. An observation study of oxygen administration to critically ill children admitted to Holy Innocents Children’s Hospital in Mbarara, Uganda
Bibliographic record
Abstract
Dataset Description: This dataset contains materials from Smart O2 study. Materials include the study protocol and associated documents. Background: Pneumonia, sepsis, and diarrheal diseases are the leading cause of critical illness and death in children worldwide. A significant proportion of these deaths can be prevented by supplemental oxygen therapy. Children in low- and middle-income countries (LMICs) bear a disproportionately higher burden of critical illness and death due to pneumonia, sepsis and diarrheal diseases but also have inequitable access to medical oxygen. While many efforts are underway to increase oxygen production capacity in underserved areas, this must be balanced with efforts to prevent waste and optimize oxygen utilization (oxygen conservation). To address the challenge of oxygen conservation, we aim to develop and evaluate a system that will allow us to adjust oxygen flow rates based on pulse oximetry feedback. This study involves the first phase: we conducted an observational study to collect baseline data from a cohort of 200 critically ill children receiving oxygen treatment at Holy Innocents Children’s Hospital. We will use this data to develop and validate a clinical algorithm and decision support system (Smart O2) for oxygen administration based on models of optimized oxygen utilization. Methods: Trained research nurses reviewed all children admitted for oxygen therapy within 72 hours of admission. Eligible children were those admitted to the hospital ward and prescribed nasal oxygen therapy by their treating healthcare provider due to low oxygen saturation (SpO₂) at the time of presentation. Children who received oxygen for seizures or other conditions in which oxygen is administered despite normal SpO2, were recommended for exclusion by a clinician for any reason, did not provide verbal assent, or had guardians who did not provide consent were excluded. Data collection for all enrolled children began immediately upon initiation of oxygen therapy. A deferred consent process was used to avoid delays in starting treatment, where consent was obtained when the child was clinically stable after oxygen therapy initiation. Consent was obtained the following day for children admitted overnight. If either the caregiver or the child declined participation, monitoring and oxygen therapy continued as per hospital protocol, but data collection was stopped, and all collected data were deleted. Nurses collected clinical characteristics and clinical data variables required for the study using a paper-based case report form (CRF). Deidentified data from the CRF were entered into a Research Electronic Data Capture (REDCap) project. The study used three data collection setups. One setup included a Masimo Root device with a Masimo ISA OR+ side-stream oxygen analyzer and a Masimo Radical-7 monitor. The other two setups used Masimo Rad-97 Pulse CO-Oximeters. All setups were equipped with custom-built in-line electronic flow meters. A custom Android application was used to record and store data in real time. The data collection devices captured physiological and system-level variables. Ethics Declaration: Ethics approval for this study was obtained from the institutional review boards at Makerere University School of Public Health in Uganda (SPH-2023-453), the Uganda National Council of Science and Technology (HS3390ES), and the University of British Columbia Children’s and Women’s Research Ethics Board in Canada (H23-01811).
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".