De novo <scp>HBV</scp> infection in a <scp>M</scp>ayo <scp>C</scp>linic hemodialysis population: Economic impact of reduced <scp>HBV</scp> testing and a call for changes in current <scp>US CDC</scp> guidelines on <scp>HBV</scp> testing protocols
Bibliographic record
Abstract
Hemodialysis (HD) exposes end-stage renal disease patients to significantly higher risks for Hepatitis B Virus (HBV) infection, a major public health scourge. Therefore, current US CDC guidelines, last revised in 2001, call for monthly HbsAg tests. The charge to Medicare per HbsAg test is $100. In an economic analysis, we hypothesized that in the new environment of Medicare Fee Bundling, this is unwise and wasteful if de novo HBV infection rate among HD patients is <1%. We determined de novo HBV infection rate among a Mayo Clinic HD cohort, July 2000-July 2010. A retrospective analysis of all relevant medical records of the cohort was completed to identify de novo HBV infection. Nine hundred sixty-five HD patients were analyzed. One case of de novo HBV infection was identified in a 54-year old known IV drug user, a previous Hepatitis C carrier. This translates to a de novo HBV case incidence rate of 0.1%. De novo HBV infection among HD patients in the US, 2000-2010, is only 0.1%. In the early 1970s, rates were as high as 30%. We recommend 3-monthly HbsAg testing, but to continue current monthly testing for IV drug users and other high-risk groups. Huge cost savings would result, without any compromise of quality outcomes. With over 500,000 HD patients, this represents a mind-boggling $40 billion savings in Medicare charges over 10 years. The US CDC should revise these outdated guidelines, last revised in 2001, to fall in line with current clinical realities on the ground.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".