Low-value clinical practices in injury care: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Preventable injuries lead to 200 000 hospital stays, 60 000 disabilities, and 13 000 deaths per year in Canada with direct costs of $20 billion. Overall, potentially unnecessary medical interventions are estimated to consume up to 30% of healthcare resources and may expose patients to avoidable harm. However, little is known about overuse for acute injury care. We aim to identify low-value clinical practices in injury care. METHODS AND ANALYSIS: We will perform a scoping review of peer-reviewed and non-peer-reviewed literature to identify research articles, reviews, recommendations and guidelines that identify at least one low-value clinical practice specific to injury populations. We will search Medline, EMBASE, COCHRANE central, and BIOSIS/Web of Knowledge databases, websites of government agencies, professional societies and patient advocacy organisations, thesis holdings and conference proceedings. Pairs of independent reviewers will evaluate studies for eligibility and extract data from included articles using a prepiloted and standardised electronic data abstraction form. Low-value clinical practices will be categorised using an extension of the Agency for Healthcare Research and Quality conceptual framework and data will be presented using narrative synthesis. ETHICS AND DISSEMINATION: Ethics approval is not required as original data will not be collected. This study will be disseminated in a peer-reviewed journal, international scientific meetings, and to knowledge users through clinical and healthcare quality associations. This review will contribute new knowledge on low-value clinical practices in acute injury care. Our results will support the development indicators to measure resource overuse and inform policy makers on potential targets for deadoption in injury care.
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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 | 0.134 | 0.104 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.016 |
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".