Economic evaluation of intrahospital clinical practices in injury care: protocol for a 10-year systematic review
Bibliographic record
Abstract
INTRODUCTION: Underuse of high-value clinical practices and overuse of low-value practices are major sources of inefficiencies in modern healthcare systems. Injuries are second only to cardiovascular disease in terms of acute care costs but data on the economic impact of clinical practices for injury admissions are lacking. This study aims to summarise evidence on the economic value of intrahospital clinical practices for injury care. METHODS AND ANALYSIS: We will perform a systematic review to identify research articles in economic evaluation of intrahospital clinical practices in acute injury care. We will search MEDLINE and databases such as Embase, Web of Science, NHS Economic Evaluation Database, Cochrane CENTRAL, BIOSIS and CINAHL for randomised or non-randomised controlled trials and observational studies using a combination of keywords and controlled vocabulary. We will consider the following outcomes relative to economic evaluations: incremental cost-effectiveness ratio, incremental cost-utility ratio, incremental net health benefit, incremental net monetary benefit (iNMB) and incremental cost-benefit ratio. Pairs of independent reviewers will evaluate studies that meet eligibility criteria and extract data from included articles using an electronic data extraction form. All outcomes will be converted into iNMB. We will report iNMB for practices classified by type of practice (hospitalisation, consultation, diagnostic, therapeutic-surgical, therapeutic-drugs, therapeutic-other). Results obtained with a ceiling ratio of $50 000 per quality-adjusted life year gained for identified clinical practices will be summarised by charting forest plots. In line with Cochrane recommendations for systematic reviews of economic evaluations, meta-analyses will not be conducted. ETHICS AND DISSEMINATION: Ethics approval is not required as original data will not be collected. This study will summarise existing evidence on the economic value of clinical practices in injury care. Results will be used to advance knowledge on value-based care for injury admissions and will be disseminated through a peer-reviewed article, international scientific meetings and clinical and healthcare quality associations.
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 | 0.096 | 0.131 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.029 | 0.025 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.060 | 0.008 |
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".