A review of the clinical value of mechanical ventilators and extracorporeal membrane oxygenation (ECMO) equipment
Bibliographic record
Abstract
Acute healthcare providers operate large, diverse medical equipment inventories. Resources for managing these inventories is frequently scarce so must be prioritised such that maximum benefit is conferred per unit of expenditure. This review identifies publications which have discussed the clinical value conferred by mechanical ventilation (MV) and by extra corporeal membrane oxygenation (ECMO). Respectively, mechanical ventilators and ECMO units are necessary to deliver these therapies. Systematic searches for publications which discuss the clinical value conferred by MV and by ECMO were conducted. The identified articles included reviews, prospective studies, retrospective studies, and models. Most presented findings in terms of the cost-effectiveness ratio. The patient populations studied, and analytical methods used varied widely. The clinical value conferred by MV varied with dependencies on several factors including the age- and disease- profile of the patient population. It was not possible to infer these dependencies from the literature which exists for ECMO. More relevant literature existed for MV, the more mature technology, than did for ECMO. The ECMO literature also tended to be more recent and included more modelling studies and fewer prospective studies. The data extracted could inform estimates of the clinical value likely to be delivered by mechanical ventilators operated by a specific institution. Estimates for ECMO are likely to carry greater uncertainty than those for MV.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".