Choosing which in-hospital laboratory tests to target for intervention: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Some laboratory testing practices may be of low value, leading to wasted resources and potential patient harm. Our scoping review investigated factors and processes that developers report using to inform decisions about what tests to target for practice improvement. METHODS: We searched Medline on May 30th, 2019 and June 28th, 2021 and included guidelines, recommendation statements, or empirical studies related to test ordering practices. Studies were included if they were conducted in a tertiary care setting, reported making a choice about a specific test requiring intervention, and reported at least one factor informing that choice. We extracted descriptive details, tests chosen, processes used to make the choice, and factors guiding test choice. RESULTS: From 114 eligible studies, we identified 30 factors related to test choice including clinical value, cost, prevalence of test, quality of test, and actionability of test results. We identified nine different processes used to inform decisions regarding where to spend intervention resources. CONCLUSIONS: Intervention developers face difficult choices when deciding where to put scarce resources intended to improve test utilization. Factors and processes identified here can be used to inform a framework to help intervention developers make choices relevant to improving testing practices.
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 | 0.038 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".