Investigating the Influence of Patient Eligibility Characteristics on the Number of Deferrable Rheumatologist Visits: Planning for a Patient-Initiated Follow-Up Strategy
Bibliographic record
Abstract
OBJECTIVE: Patient-initiated follow-up (PIFU) for rheumatoid arthritis (RA) is a model of care delivery wherein patients contact the clinic when needed instead of having regularly scheduled follow-up. Our objective was to investigate the influence of different patient eligibility characteristics on the number of potentially deferred visits to inform future implementation of a PIFU strategy. METHODS: We conducted a retrospective chart review of 7 rheumatologists' practices at 2 university-based clinics between March 1, 2021, and February 28, 2022. Data extracted included the type and frequency of visits, disease management, comorbidities, and care complexities. Stable disease was defined as remission or low disease activity with no medication changes at all visits. The influence of patient characteristics on the number of deferrable visits in patients with stable disease was explored in 4 criteria sets that were based on early disease duration, medication prescribed, presence of care complexity elements, and comorbidity burden. RESULTS: Records from 770 visits were reviewed from 365 patients with RA (71.5% female, 70% seropositive). Among all criteria sets, the proportion of visits that could be redirected varied between 2.5% and 20.9%. The highest proportion of deferrable visits was achieved when eligibility criteria included only stable disease activity and patients with RA on conventional synthetic disease-modifying antirheumatic drugs or no medications (n = 161, 20.9%). CONCLUSION: PIFU may result in a more efficient use of specialist healthcare resources. However, the applicability of such models of care and the number of deferred visits is highly dependent on patient characteristics used to establish eligibility criteria for that model. These findings should be considered when planning implementation trials.
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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 | 0.033 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".