The development and implementation of an oxygen treatment solution for health facilities in low and middle-income countries.
Bibliographic record
Abstract
BACKGROUND: Oxygen reduces mortality from severe pneumonia and is a vital part of case management, but achieving reliable access to oxygen is challenging in low and middle-income country (LMIC) settings. We developed and field tested two oxygen supply solutions suitable for the realities of LMIC health facilities. METHODS: A Health Needs Assessment identified a technology gap preventing reliable oxygen supplies in Gambian hospitals. We used simultaneous engineering to develop two solutions: a Mains-Power Storage (Mains-PS) system consisting of an oxygen concentrator and batteries connected to mains power, and a Solar-Power Storage (Solar-PS) system (with batteries charged by photovoltaic panels) and evaluated them in health facilities in The Gambia and Fiji to assess reliability, usability and costs. RESULTS: The Mains-PS system delivered the specified ≥85% (±3%) oxygen concentration in 100% of 1-2 weekly measurements over 12 months, which was available to 100% of hypoxaemic patients, and 100% of users rated ease-of-use as at least 'good' (90% very good or excellent). The Solar-PS system delivered ≥85% ± 3%) oxygen concentration in 100% of 1-2 weekly measurements, was available to 100% of patients needing oxygen, and 100% of users rated ease-of-use at least very good.Costs for the systems (in US dollars) were: PS$9519, Solar-PS standard version $20 718. The of oxygen for a standardised 30-bed health facility using 1.7 million litres of oxygen per year was: for cylinders 3.2 cents (c)/L in The Gambia and 6.8 c/L in Fiji, for the PS system 1.2 c/L in both countries, and for the Solar-PS system 1.5 c/L in both countries. CONCLUSIONS: The oxygen systems developed and tested delivered high-quality, reliable, cost-efficient oxygen in LMIC contexts, and were easy to operate. Reliable oxygen supplies are achievable in LMIC health facilities like those in The Gambia and Fiji.
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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 | 0.000 | 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".