Acute hepatitis after starting zinc therapy in a patient with presymptomatic Wilson’s disease
Bibliographic record
Abstract
To the Editor: I read with great interest and more than a little dismay the article by Myers, Gregor, and Marotta 1 on the cost-effectiveness of hepatitis A vaccination in patients with chronic hepatitis C. While I found the article to be very well written and thorough, I find the premise for the article to be totally unacceptable.Are we to withhold a simple vaccination from our patients, knowing that they run the risk of severe morbidity or death simply because it may not be "cost effective" in a population study?This article calls to mind a story that was recently played out in the courts in the United States.There was once a large automobile manufacturer that produced a vehicle it knew was unsafe.Not only did they know that it was unsafe, they also knew how to fix the problem to render the vehicle safe.However, rather than do the right thing and fix the vehicle (at a cost of $11 per vehicle), they embarked on a course similar to Myers et al.They first calculated the cost of correcting the defect and then compared it with the cost they may incur if the defect was not corrected.They used data from the federal government and that the National Highway Traffic Safety Administration made available to them.They calculated the average damage per person killed by their vehicle.They added medical costs, property damage, insurance administration, legal and court costs, employer losses, victim pain and suffering, and funeral bills as well as "other costs."When they found that this cost would be less than the cost of correcting the defect on the vehicle, they elected to take their chances in court.Their strategy ultimately landed them in court and resulted in one of the largest settlements in the American legal system.The ethics involved in their decision-making process was headline news throughout the nation.The parallels between this story and the conclusions drawn from this article I find to be quite remarkable.I can see myself in the future consoling Mrs. X upon the death of her husband from hepatitis A superimposed on hepatitis C, a disease that could have been prevented by a simple vaccination, by handing her the article and saying, "but as you can see here, clearly it would not have been cost effective for me to have given your husband a simple vaccine."I was surprised that this article did not provoke more editorial comments from the editors of HEPATOLOGY and I certainly hope that the ACIP does not reconsider its stance on vaccinating all patients with hepatitis C with the hepatitis A vaccine.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".