Low-value clinical practices in injury care: A scoping review and expert consultation survey
Bibliographic record
Abstract
BACKGROUND: Tests and treatments that are not supported by evidence and could expose patients to unnecessary harm, referred to here as low-value clinical practices, consume up to 30% of health care resources. Choosing Wisely and other organizations have published lists of clinical practices to be avoided. However, few apply to injury and most are based uniquely on expert consensus. We aimed to identify low-value clinical practices in acute injury care. METHODS: We conducted a scoping review targeting articles, reviews and guidelines that identified low-value clinical practices specific to injury populations. Thirty-six experts rated clinical practices on a five-point Likert scale from clearly low value to clearly beneficial. Clinical practices reported as low value by at least one level I, II, or III study and considered clearly or potentially low-value by at least 75% of experts were retained as candidates for low-value injury care. RESULTS: Of 50,695 citations, 815 studies were included and led to the identification of 150 clinical practices. Of these, 63 were considered candidates for low-value injury care; 33 in the emergency room, 9 in trauma surgery, 15 in the intensive care unit, and 5 in orthopedics. We also identified 87 "gray zone" practices, which did not meet our criteria for low-value care. CONCLUSION: We identified 63 low-value clinical practices in acute injury care that are supported by empirical evidence and expert opinion. Conditional on future research, they represent potential targets for guidelines, overuse metrics and de-implementation interventions. We also identified 87 "gray zone" practices, which may be interesting targets for value-based decision-making. Our study represents an important step toward the deimplementation of low-value clinical practices in injury care. LEVEL OF EVIDENCE: Systematic Review, Level IV.
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 | 0.125 | 0.404 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.041 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".