Key performance indicators in extracorporeal membrane oxygenation (ECMO): protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Extracorporeal membrane oxygenation (ECMO) is an intervention used in critically ill patients with severe cardiopulmonary failure that is expensive and resource intensive and requires specialised care. There remains a significant practice variation in its application. This systematic review will assess the evidence for key performance indicators (KPIs) in ECMO. METHODS AND ANALYSIS: We will search Ovid MEDLINE, Ovid EMBASE, Cumulative Index to Nursing and Allied Health Literature and the Cochrane Library including the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials and databases from the National Information Center of Health Services Research and Health Care Technology, for studies involving KPIs in ECMO. We will rate methodological quality using the Newcastle-Ottawa Quality Assessment Scale. Randomized controlled trials (RCTs) will be evaluated with the Cochrane Risk of Bias tool, and qualitative studies will be evaluated using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN checklist). Grey literature sources will be searched for technical reports, practice guidelines and conference proceedings. We will identify relevant organisations, industry leaders and non-profit organisations that represent key opinion leads in the use of ECMO. We will search the Agency of Healthcare Research and Quality National Quality Measures Clearinghouse for ECMO-related KPIs. Studies will be included if they contain quality measures that occur in critically ill patients and are associated with ECMO. The analysis will be primarily descriptive. Each KPI will be evaluated for importance, scientific acceptability, utility and feasibility using the four criteria proposed by the US Strategic Framework Board for a National Quality Measurement and Reporting System. Finally, KPIs will be evaluated for their potential operational characteristics, their potential to be integrated into electronic medical records and their affordability, if applicable. ETHICS AND DISSEMINATION: Ethical approval is not required as no primary data will be collected. Findings will be published in a peer-reviewed journal and presented at academic. PROSPERO REGISTRATION NUMBER: 9 August 2022. CRD42022349910.
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 | 0.069 | 0.109 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.009 |
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".