Note of Republication: A Prospective International Study on Adherence to Treatment in 305 Patients With Flaring SLE: Assessment by Drug Levels and Self‐Administered Questionnaires
Bibliographic record
Abstract
Nonadherence to treatment is a major cause of lupus flares. Hydroxychloroquine (HCQ), a major medication in systemic lupus erythematosus, has a long half-life and can be quantified by high-performance liquid chromatography. This international study evaluated nonadherence in 305 lupus patients with flares using drug levels (HCQ < 200 ng/ml or undetectable desethylchloroquine), and self-administered questionnaires (MASRI < 80%). Drug levels defined 18.4% of the patients as severely nonadherent. In multivariate analyses, younger age, nonuse of steroids, higher body mass index, and unemployment were associated with nonadherence by drug level. Questionnaires classified 23.4% of patients as nonadherent. Correlations between adherence measured by questionnaires, drug level, and physician assessment were moderate. Both methods probably measured two different patterns of nonadherence: self-administered questionnaires mostly captured relatively infrequently missed tablets, while drug levels identified severe nonadherence (i.e., interruption or erratic tablet intake). The frequency with which physicians miss nonadherence, together with underreporting by patients, suggests that therapeutic drug monitoring is useful in this setting. (Trial registration: ClinicalTrials.gov: NCT01509989.).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".