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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".