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Record W2903337420 · doi:10.1111/ctr.13456

Marijuana use in transplantation: A call for clarity

2018· review· en· W2903337420 on OpenAlexaff
Marilyn E. Levi, Brian T. Montague, Christian Thurstone, Deepali Kumar, Shirish Huprikar, Camille N. Kotton

Bibliographic record

VenueClinical Transplantation · 2018
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity Health Network
FundersAmerican Society of Transplantation
KeywordsMedicineTransplantationCLARITYRecreationCannabisFamily medicineOrgan transplantationIntensive care medicineEnvironmental healthSurgeryPsychiatry

Abstract

fetched live from OpenAlex

Transplant centers have varying policies for marijuana (MJ) use in donors, transplant candidates, and recipients. Rationales for these differences range from concerns for fungal complications, impaired adherence, and drug interactions. This paper reviews the current status of MJ policies and practices in transplant centers and results of a survey sent to the American Society of Transplantation (AST) membership by the Executive Committee of the AST Infectious Diseases Community of Practice.The purpose of the survey was to compare policies and concerns of MJ use to actual observed complications. Of the 3321 surveys sent, 225 members (8%) responded. Transplant centers varied in their approval processes, differing even in organ types within the same institutions. Furthermore, there was discordance among transplant centers in their perceived risks of marijuana use as opposed to complications actually observed. An increasing number of states continue to legalize medical and recreational MJ resulting in widespread availability. Further research is needed to assess the validity of concerns for complications of MJ use in potential donors and recipients. Ultimately, standardized guidelines should be established based on studies and evidence-based criteria to assist transplant programs in their policies around the use of cannabis in their donors and recipients.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.275
GPT teacher head0.503
Teacher spread0.228 · 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 designNot applicable
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

Citations39
Published2018
Admission routes1
Has abstractyes

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