A low-cost initiative to reduce duplicate hepatitis B virus serological testing
Bibliographic record
Abstract
BACKGROUND: Currently, multiple clinical laboratories provide serological testing for hepatitis B virus (HBV) in Alberta, Canada. Concerns were raised regarding single serology specimens having duplicate testing performed during the specimen referral process from one laboratory to another. In an attempt to reduce duplicate testing for anti-HBs and HBsAg markers, we implemented a stamp on paper requisitions to identify if testing had already been performed on referred specimens. We aimed to determine the number of duplicate tests and cost of duplicate testing pre- and post-stamp implementation. STUDY DESIGN: The requisition stamp was implemented between May and August 2016. HBV serology testing results from two clinical laboratories between January 01, 2015 and December 31, 2017 (n = 803,637) were examined. The number of tests performed on the same individual within a 3-day window was identified and the associated costs were determined. RESULTS: After stamp implementation, duplicated HBsAg and anti-HBs tests decreased from 20.8% (n = 28,545) and 18.4% (n = 20,151) to 3.7% (n = 4,604) and 2.5% (n = 2,593), respectively. This represented an estimated annual savings of $86,427 and $82,522 CAD in supply costs for HBsAg and anti-HBs tests, respectively. CONCLUSIONS: The requisition stamp initiative was effective in reducing the number of duplicate tests performed between two laboratory sites. This low-cost intervention could be applied to other testing situations, including other highly duplicated serological markers, which may have broad reaching cost-saving effects for laboratory testing.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".