The effect of a hepatitis serology testing algorithm on laboratory utilization
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Laboratory testing algorithms use patient and laboratory data to identify the optimal testing strategy for patients. Studies have shown that these algorithms can decrease test utilization. Since over-utilization of hepatitis serological tests was suspected, a hepatitis serology testing algorithm was initiated in Ontario, Canada. This study determined the effects of this algorithm on utilization. METHODS: Population-based retrospective observational study, involving all patients having viral hepatitis serological testing at private laboratories in Ontario, Canada, between July 1991 and December 1999. Prior to the testing algorithm, physicians listed the required specific antigens and antibodies on the test requisition form. In September 1996, the form was changed so that physicians identified the clinical indication--acute hepatitis, chronic hepatitis or immune status testing--for hepatitis serology testing. Therefore, the algorithm introduced a new 'tick-box' to the requisition form. Tests conducted by the laboratory depended upon which indication was chosen. Rates for hepatitis serological testing were calculated using population-based claims data. RESULTS: Time-series modelling showed that the testing algorithm was associated with a slight but significant increase in the use of hepatitis serology (P < 0.05). The algorithm was associated with an increase of 30 serological tests per 100,000 population per month. CONCLUSIONS: Introducing a testing algorithm for hepatic viral serology in Ontario did not significantly decrease hepatitis serology utilization and may be associated with a slight, but significant, increase in serology utilization rates.
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 | 0.013 | 0.084 |
| 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) | 0.002 | 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".