Methotrexate and Injectable Tumor Necrosis Factor-α Inhibitor Adherence and Persistence in Children with Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: To measure adherence and persistence with methotrexate (MTX) and injectable tumor necrosis factor-α (iTNF-α) inhibitors (etanercept, adalimumab) among children prescribed these medications by a rheumatologist. METHODS: Data were obtained from a US pharmacy benefits management firm. Children were included if they were < 18 years of age, had ≥ 1 prescription claim between January 2009 and December 2010 for MTX or an iTNF-α inhibitor that was prescribed by an adult or pediatric rheumatologist. The medication possession ratio (MPR) was calculated for each medication, with MPR ≥ 80% indicating good adherence. MPR were compared by route of administration, age, and by new users versus continuing users. Persistence was measured for new users of each medication from initiation until discontinuation, or for a maximum of 1 year. RESULTS: A total of 1964 children were included. The majority of children had MPR < 80%. Children taking subcutaneous MTX had the lowest mean MPR [46.9%; median 44.9%; interquartile range (IQR) 23%-69.6%] and the lowest persistence, with 26% of children continuing the medication at 1 year. Mean MPR was highest for iTNF-α (65.7%; median 70.1%; IQR 46%-89.3%), as was persistence, with 52% of children continuing the medication at 1 year. Children age < 13 years tended to have higher MPR, but this was statistically significant only for oral MTX (61.1% vs 54.9% in children age ≥ 13 yrs; p = 0.02). CONCLUSION: Adherence and persistence in this cohort varied by medication and route of administration. Both outcomes are important considerations for physicians prescribing these medications in routine clinical care and for the assessment of treatment effectiveness in the research setting.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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.
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".