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Record W2779583727 · doi:10.1111/ajt.14635

Meeting report: FDA public meeting on patient-focused drug development and medication adherence in solid organ transplant patients

2017· article· en· W2779583727 on OpenAlexaff
Robert B. Ettenger, Renata Albrecht, Rita R. Alloway, Ozlem Belen, M Cavaillé‐Coll, Marie A. Chisholm‐Burns, Mary Amanda Dew, William E. Fitzsimmons, Peter Nickerson, Graham C. Thompson, Pujita Vaidya

Bibliographic record

VenueAmerican Journal of Transplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineOrgan transplantationTransplantationPsychological interventionIntensive care medicineBroad spectrumFamily medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

The Food and Drug Administration (FDA) held a public meeting and scientific workshop in September 2016 to obtain perspectives from solid organ transplant recipients, family caregivers, and other patient representatives. The morning sessions focused on the impact of organ transplantation on patients' daily lives and the spectrum of activities undertaken to maintain grafts. Participants described the physical, emotional, and social impacts of their transplant on daily life. They also discussed their posttransplant treatment regimens, including the most burdensome side effects and their hopes for future treatment. The afternoon scientific session consisted of presentations on prevalence and risk factors for medication nonadherence after transplantation in adults and children, and interventions to manage it. As new modalities of Immunosuppressive Drug Therapy are being developed, the patient perceptions and input must play larger roles if organ transplantation is to be truly successful.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0110.007
Insufficient payload (model declined to judge)0.0820.020

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 head0.020
GPT teacher head0.289
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations54
Published2017
Admission routes1
Has abstractyes

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