Patient Preference and Willingness to Pay for Transient Elastography versus Liver Biopsy: A Perspective from British Columbia
Bibliographic record
Abstract
BACKGROUND: The cost of liver biopsy (LB) is publicly funded in British Columbia, while the cost of transient elastography (FibroScan [FS], Echosens, France) is not. Consequently, there is regional variation regarding FS access and monitoring of liver disease progression. OBJECTIVE: To evaluate patient preference for FS versus LB and to assess the willingness to self-pay for FS. METHODS: Questionnaires were distributed in clinic and via mail to LB-experienced and LB-naive patients who underwent FS at Vancouver General Hospital, Vancouver, British Columbia. RESULTS: The overall response rate was 76%. Of the 422 respondents, 205 were LB-experienced. The mean age was 53.5 years, 50.2% were male, 54.7% were Caucasian, 38.2% had hepatitis C and 26.3% had an annual household income >$75,000. Overall, 95.4% of patients preferred FS to LB. FS was associated with greater comfort than LB, with the majority reporting no discomfort during FS (84.1% versus 7.8% for LB), no discomfort after (96.2% versus 14.6% LB) and no feelings of anxiety after FS explanation (78.2% versus 12.7% LB). FS was also associated with greater speed, with the majority reporting short test duration (97.2% versus 48.3% LB) and short wait for the test result (95.5% versus 30.2% LB). Most (75.3%) respondents were willing to self-pay for FS, with 26.3% willing to pay $25 to $49. Patients with unknown liver disease preferred LB (OR [FS preference] 0.20 [95% CI 0.07 to 0.53]). CONCLUSIONS: FS was the preferred method of assessing liver fibrosis among patients, with the majority willing to self-pay. To ensure consistency in access, provincial funding for FS is needed. However, LB remains the procedure of choice for individuals with an unknown diagnosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".