Population-based Study of Repeat Laboratory Testing
Bibliographic record
Abstract
BACKGROUND: Test repetition could be a readily modifiable component of laboratory utilization. Laboratory test repetition has not been rigorously studied at a population-based level. Our objective was to determine the prevalence of, and charges associated with, repetition of eight common laboratory tests. METHODS: We performed a cross-sectional study using high-quality, population-based clinical databases that included adults in Eastern Ontario, Canada, between September 1999 and September 2000 for incidence of repeating eight common laboratory tests (hemoglobin, sodium, creatinine, thyrotropin, total cholesterol, HDL-cholesterol, ferritin, and hemoglobin A(1C)). Tests were classified as potentially redundant if repeated within the test's baseline testing interval. For creatinine, sodium, and hemoglobin, only tests repeated in the community were considered. For a sensitivity analysis, we varied the repeat interval by 25%, excluded tests repeated by different physicians, and excluded repeats of normal tests. RESULTS: Almost 4 million tests were conducted during the study year. Most tests (76%) were conducted on patients in the community. More than one-half of all people in the population had at least one laboratory test, with an overall testing rate of 367 tests per 100 people per year. Repeat testing within 1 month accounted for 30% of all utilization (109 repeat tests per 100 people per year). Repetition was more common in hospitalized patients, varied extensively among tests, and was concentrated in a limited number of people. For the eight tests included in the study, charges of potentially redundant repetition in adults totaled between 13.9 and 35.9 million dollars (Canadian) annually. CONCLUSIONS: Laboratory test repetition is very common, makes up a significant component of overall test utilization, and is costly.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".