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Measuring compliance with drug regimens after renal transplantation: comparison of self-report and clinician rating with electronic monitoring

2004· article· en· W2076902840 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTransplantation · 2004
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsMedical Council of Canada
Fundersnot available
KeywordsMedicineConfidentialityTransplantationRenal transplantMedication adherenceCompliance (psychology)Drug complianceIntensive care medicineClinical PracticeKidney transplantationInternal medicineFamily medicinePsychology

Abstract

fetched live from OpenAlex

Nonadherence to immunosuppressants in renal transplant recipients is a major factor affecting graft survival, but it is difficult to detect accurately in clinical practice. Adherence was measured in 153 adult renal transplant recipients using self-report questionnaires and interview, clinician rating, and cyclosporine levels. The sensitivity and specificity of these measures were determined by comparison with electronic monitoring in a randomly selected subsample of 58 subjects. Measures of adherence in current clinical use do not perform well when tested against electronic monitoring. Self-report at a confidential interview was the best measure of adherence for the detection of both missed doses and erratic timing of medication. However, the use of a confidential interview is not directly applicable to a clinical setting. Further research on how best to facilitate disclosure in clinical settings may be the best way to develop adherence measures for use in routine practice.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.317
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