Poster Session 3: Cost-Effectiveness; HCV: Diagnostics, Epidemiology, and Natural History
Bibliographic record
Abstract
mal protocol to define candidacy for down-staging, 27.5% did not.No specific threshold in terms of number and size of tumors was used to define candidacy for down-staging by 42.5% of programs.Portal vein tumor thrombus was not a contra-indication to down-staging for 27.5% of programs.Most programs (84%) did not use a specific alpha fetoprotein (AFP) threshold to determine down-staging candidacy.For those programs which used an AFP threshold, this ranged from 400 to 5000.Almost all programs (97%) used the Milan Criteria to define down-staging success.A period of observation to demonstrate stability after successful down-staging was not required by 30%.When a period of stability was required, this ranged from 1 to 9 months, with 3 months being most common.Most programs considered trans-arterial radio-embolization as a down-staging modality, but chemo-embolization remained the dominant modality in all but a single program.Overall, most respondents expressed neutral feelings towards each of these practices, although 25% felt that use of the Milan Criteria to define successful down-staging was too restrictive.Conclusions: Practices vary across the country in terms of defining candidacy for down-staging, and for defining success of down-staging.Despite the range of practices, most individual practitioners reported comfort with the practice pattern at their own institution.Data on the efficacy of down-staging should be interpreted in the context of this wide range of down-staging practices.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.246 | 0.036 |
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".