Effect of Provider Continuity on Test Repetition
Bibliographic record
Abstract
BACKGROUND: Provider continuity (PC) occurs when a patient is treated by the same physician over time. A perceived benefit of PC is decreased test repetition. Repeat tests make up a significant proportion of overall laboratory utilization. This study determined whether test repetition increases when PC decreases. METHODS: Cohort study of adults in eastern Ontario, Canada between September 1999 and September 2000 using population-based clinical databases. The primary study outcome was the probability that 7 common laboratory tests (hemoglobin, sodium, creatinine, thyrotropin, total cholesterol, ferritin, and hemoglobin A1C) were repeated at physician visits subsequent to the index test. We determined whether the probability of test repetition changed if the follow-up physician ordered the index test. We adjusted for multiple factors regarding the patient (age, sex, days in hospital, and number of physician visits in previous year), index test (normality and location), follow-up visit (location and time from index test), and follow-up physician (age and specialty). RESULTS: The study included 881,353 patients, 1,419,438 index laboratory tests, and 7,622,938 follow-up physician visits. After adjusting for other important factors, we found that tests were significantly more likely to be repeated if the follow-up physician ordered the index test (adjusted odds ratio range 2.5-5.9). This association was consistent in most subgroups. CONCLUSIONS: For these common laboratory investigations, PC was associated with increased, not decreased, test repetition. This suggests that increased PC alone will likely not decrease test utilization.
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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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".