Transient Elastography in Canada: Current State and Future Directions
Bibliographic record
Abstract
BACKGROUND: Transient elastography (TE) is a safe and effective technology to noninvasively assess hepatic fibrosis in patients with numerous liver conditions. TE is not readily available to all Canadians, and data regarding how this technology is incorporated into clinical practice are lacking. OBJECTIVE: To describe TE practices in Canada, and to identify strategies to optimize access and usage. METHODS: All Canadian centres with TE devices were invited to complete a survey after obtaining purchasing data from the national distributor of the device. Descriptive statistics were generated. RESULTS: Forty-two devices were available in Canada as of January 2015. Seventy-one percent are used in academic settings, 74% are hospital based and 26% are in private clinics. The test is performed by trained nurses in 48% of centres, physicians in 19%, technicians in 9.5% and by any member of the health care team in 19%. Nineteen percent of centres provide satellite clinics to perform the test. While the majority of the centres perform the test at no additional cost to patients, 29% charge a variable fee. CONCLUSION: In Canada, most TE devices are used in academic and⁄or hospital-based settings, thus limiting access to this technology to many patients. A sizeable minority of centres mandate patients pay variable out-of-pocket fees. Satellite clinics offered by some centres could increase access, but are not widespread. The lack of uniformity with TE practices in Canada suggests that a national policy is needed.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".