A Review of Medical Oxygen Concentrators for Respiratory Applications
Bibliographic record
Abstract
Medical oxygen concentrators are vital devices that deliver supplemental oxygen to persons with hypoxemia. This narrative review provides a review of oxygen concentrators, outlining their operational mechanisms, classification, evaluation criteria, market accessibility, advantages, disadvantages, and prospective developments. Critical insights encompass the dominance of pressure swing adsorption technology in oxygen separation, the increasing demand for portable and energy-efficient models, and the promise of smart technologies and new materials to improve oxygen therapy. The analysis underscores the necessity of tackling issues of accessibility and cost, especially in resource-constrained environments. This review highlights the essential function of oxygen concentrators in potentially fulfilling the United Nations Sustainable Development Goals 3, 8, 12, 13, and 17 by improving health care accessibility, fostering economic growth, advancing environmental sustainability, and facilitating global partnerships. The incorporation of automated controls and artificial intelligence-driven modifications may become important for customizing oxygen administration to meet patient requirements and fluctuating conditions, hence ensuring optimal therapy and reducing the workload of health care providers. Finally, it is emphasized that the necessity of addressing issues with accessibility and cost, especially in resource-constrained environments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.009 | 0.003 |
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".