Bibliographic record
Abstract
Background: Minimum retesting intervals (MRI) are a popular demand management solution for the identification and reduction of over-utilized tests. In 2011 Association of Clinical Biochemistry and Laboratory Medicines (ACB) published evidence-based recommendations for the use of MRI. Aim: The aim of the paper was to review the use of MRI over the period since the introduction of these recommendations in 2011 to 2020 and compare it to previous published data between 2000-2010. Methods: A multi-source literature search was performed to identify studies that reported the use of a MRI in the management or identification of inappropriate testing between the years prior to (2000-2010) and after implementation (2011-2020) of these recommendations. Results: 31 studies were identified which met the acceptance criteria (2000-2010 n=4, 2011-2020 n=27). Between 2000 and 2010 4.6% of tests (203,104/4,425,311) were identified as failing a defined MRI which rose to 11.8% of tests (2,691,591/22,777,288) in the 2011-2020 period. For those studies between 2011 and 2020 reporting predicted savings (n=20), 14.3% of tests (1,079,972/750,580) were cancelled, representing a total saving of 2.9 M Euros or 2.77 Euro/test. The most popular rejected test was Haemoglobin A1c which accounted for nearly a quarter of the total number of rejected tests. 13 out 27 studies used the ACB recommendations. Conclusions: MRI are now an established, safe and sustainable demand management tool for the identification and management of inappropriate testing. Evidence based consensus recommendations have supported the adoption of this demand management tool into practice across multiple healthcare settings globally and harmonizing laboratory practice.
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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 | 0.050 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".