Rheumatologists’ Views and Experiences in Managing Rheumatoid Arthritis in Elderly Patients: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: In this qualitative study we analyzed the (1) influence of age, comorbidity, and frailty on management goals in elderly patients with RA; (2) experiences of rheumatologists regarding the use of the Disease Activity Score at 28 joints (DAS28) to monitor disease activity; and (3) differences in management strategies in elderly patients with RA compared to their younger counterparts. METHODS: Rheumatologists were purposively sampled for a semistructured interview. Two readers independently read and coded the interview transcripts. Important concepts were taxonomically categorized and combined in overarching themes by using NVivo 11 software. RESULTS: Seventeen rheumatologists (mean age 44.8 yrs, SD 7.7 yrs; 29% male) from 9 medical centers were interviewed. Preserving an acceptable level of functioning was the most important management goal in patients ≥ 80 years and in patients with high levels of comorbidity and frailty. The DAS28 score less frequently steered the management strategy, because rheumatologists commented that comorbidity and an age-related erythrocyte sedimentation rate elevation might distort the DAS28 score. Instead, management of elderly patients highly depended on comorbidity, frailty, and their subsequent effects such as cognitive and physical decline, dependency, and polypharmacy. Presence of 1 or more of these factors frequently resulted in a less future-oriented management approach with less emphasis on the maximal prevention of joint erosions. CONCLUSION: The treat-to-target model is not automatically adopted in the elderly patient population. Future evidence-based RA management recommendations for elderly patients with RA are needed and should account for factors such as comorbidity and frailty.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".