Medical adherence and liver transplantation: a brief review
Bibliographic record
Abstract
Liver transplantation remains the only feasible long-term treatment option for patients with end-stage liver disease. Despite significant medical and surgical advances over the decades, liver transplantation remains a complex undertaking with the need for indefinite immunosuppression and avoidance of patient behaviours that may jeopardize the allograft. Adherence (formerly called "compliance") to medical recommendations in terms of anti-rejection medications and-in the case of alcoholic liver disease, abstinence-is considered a key cornerstone to long-term allograft and patient survival. Not surprisingly, a history of habitual non-adherence is considered a contraindication to liver transplantation, especially re-transplantation. It is often assumed that non-adherence policies are "self-evidential" based on "common sense" and "expert opinion." In fact, non-adherence and its negative effects have been well studied in medicine, including in solid organ transplantation. In this review, we present the evidence that non-adherence to medical advice is clearly associated with worse medical outcomes, supporting the concept that efforts to support patient adherence post-transplant need to be optimized at all times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".