Pulse-oximetry and supplemental oxygen utilization in low-resource, pre-hospital settings: An exploratory cost-effectiveness analysis
Bibliographic record
Abstract
Pneumonia is the leading cause of death globally in children aged 0-5 years. Early access to pulse-oximetry and supplemental oxygen in low-resource, pre-hospital settings may result in improved pediatric pneumonia outcomes. However, few data exist regarding their application in such settings. We performed an exploratory cost-effectiveness analysis using a decision analysis model to examine use of pulse-oximetry and supplemental oxygen in low-resource, pre-hospital settings. Our model yielded an Incremental Cost-Effectiveness Ratio (ICER) for pre-hospital pulse-oximetry use of $229 (USD) per life-year (LY) saved compared to no pulse-oximetry use. Given that inpatient management is the standard of care for hypoxemic pneumonia, when only pre-hospital costs were considered the result was an ICER of $13/LY saved. Both values were considered cost-effective according to a strict willingness-to-pay (WTP) threshold set for the lowest GDP per capita in the world. When oxygen was analyzed in combination with pulse-oximetry, we found a baseline WTP threshold for pre-hospital oxygen of $1.18 per patient. Again, when the payer perspective included only pre-hospital costs, that WTP for oxygen rose to $26.64. For every 1% reduction in total pediatric pneumonia mortality consequent to pre-hospital oxygen use, we determined the recommended WTP allowance for oxygen would increase by approximately $1.75. We conclude that pulse-oximetry is likely cost-effective in low-resource, pre-hospital environments. We acknowledge the need for further research on the effectiveness of pre-hospital oxygen in reducing pediatric pneumonia mortality and suggest ranges of cost and efficacy for which oxygen is likely to be found cost-effective in tandem with pulse-oximetry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".