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Record W4251720915 · doi:10.1177/152692481002000303

Methadone Maintenance Therapy in Liver Transplantation

2010· review· en· W4251720915 on OpenAlexafffund
Modi Jiao, Erica D. Greanya, Mazhar Haque, Eric M. Yoshida, J Soós

Bibliographic record

VenueProgress in Transplantation · 2010
Typereview
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsVancouver General HospitalUniversity of TorontoUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineContraindicationMethadoneCirrhosisLiver transplantationTransplantationHepatitis CIntensive care medicineHeroinSurgeryInternal medicinePsychiatryDrug

Abstract

fetched live from OpenAlex

Cirrhosis due to chronic infection with hepatitis C virus remains by far the most common reason for liver transplantation in North America. Currently, parenteral use of street drugs is the most common means of acquiring hepatitis C. Methadone maintenance therapy is an accepted form of treatment for chronic opiate (eg, heroin) addiction and, not surprisingly, a significant proportion of methadone-treated patients have chronic hepatitis C. The feasibility of liver transplant candidacy in hepatitis patients who require methadone maintenance therapy is controversial, and some transplant centers require patients to withdraw from such therapy in order for the transplant process to move forward. Thus stable patients with end-stage cirrhosis who are receiving methadone maintenance are left in a most difficult situation: discontinue methadone and accept the side effects of withdrawal with the risk of recidivism to use of street opiates, an absolute contraindication for transplantation, or continue methadone therapy and risk exclusion from the transplant process. The issue of methadone replacement therapy in end-stage cirrhosis and the posttransplant literature on the subject are explored in this paper.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.408
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2010
Admission routes2
Has abstractyes

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