Improving the appropriateness of laboratory submissions for urinalysis from general practice
Bibliographic record
Abstract
BACKGROUND: Urine is the most common microbiology laboratory specimen. Submissions increase annually by 5-10%, and many specimens may be unnecessary. OBJECTIVES: To assess the impact of guidance, implemented by interactive workshops and reinforced with modified request forms, on specimen submission. METHODS: This was a prospective randomized controlled study with modified Zelen design. The study population comprised five primary care trusts (PCTs) in Gloucestershire/County Durham/Darlington, containing 82 general practices in six geographical clusters. The six clusters were randomly assigned to urine workshop covering submission in the elderly, adults and children or a control workshop. Within these groups, half the practices were randomized to receive modified laboratory forms emphasizing the workshop messages. Practices were not aware of the study. RESULTS: Workshops lead to a 12% reduction in urine submissions from 16- to 64-year olds, which persisted for the 15 months but had no effect on bacteriuria rate. Workshops had no significant effect in the elderly or children. Modified forms were not associated with any reduction in submissions but were associated with an 11% reduction in detection of significant bacteriuria in 16- to 64-year olds. CONCLUSIONS: The 12% decrease in urine submissions from 16- to 64-year olds, attained with workshops, may help counter relentlessly rising test submissions. Modified forms are currently not worth pursuing. When educational workshops are used across PCTs to change practice, the change in test submission is smaller than attained in educational initiatives involving volunteers. Workshops may be more effective if they also discuss urine submissions from asymptomatic patients and are directed at high testing practices and care homes.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".