Bibliographic record
Abstract
Hepatitis B is a serious global health problem with over 2 billion people infected worldwide and 350 million suffering from chronic hepatitis B (CHB) infection. Infection can lead to chronic hepatitis, cirrhosis and hepatocellular carcinoma (HCC) accounting for 320,000 deaths per year. Numerous treatments are available, but with a growing number of therapies each with considerable trade-offs, the optimal treatment strategy is not transparent.\n\nThis dissertation investigates the relative efficacy of treatments for CHB and estimates the health related quality of life (HRQOL) and health utilities of mild to advanced CHB patients. \n\nA systematic review of published randomized controlled trials comparing surrogate outcomes for the first year of treatment was performed. Bayesian mixed treatment comparison meta-analysis was used to synthesize odds ratios, including 95% credible intervals and predicted probabilities of each outcome comparing all currently available treatments in HBeAg-positive and/or HBeAg-negative CHB patients. Among HBeAg-positive patients, tenofovir and entecavir were most effective, while in HBeAg-negative patients, tenofovir was the treatment of choice. \nHealth state utilities and HRQOL for patients with CHB stratified by disease stage were elicited from patients attending tertiary care clinics at the University Health Network in Toronto. Respondents completed the standard gamble, EQ5D, Health Utilities Index Mark 3 (HUI3), Short-Form 36 version-2 and a demographics survey in their preferred language of English, Cantonese or Mandarin. Patient charts were accessed to determine disease stage and co-morbidities.\n\nThe study included 433 patients of which: 294 had no cirrhosis, 79 had compensated cirrhosis, 7 had decompensated cirrhosis, 23 had HCC and 30 had received liver transplants. Mean standard gamble utilities were 0.89, 0.87, 0.82, 0.84 and 0.86 for the respective disease stages. HRQOL in CHB patients was only impaired at later stages of disease. Neither chronic infection nor antiviral treatment lowered HRQOL. Patients with CHB do not experience lower HRQOL as seen in patients with hepatitis C. \nThe next step in this area of research is to incorporate the estimates synthesized by the current studies into a decision model evaluating the cost-effectiveness of treatment to provide guidance on the optimal therapy for patients with HBeAg-positive and HBeAg-negative CHB.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".