Sharing Ongoing Care with Primary Care Physicians Opens Up Opportunity for Timelier and Earlier Care by Rheumatologists for Patients with New Inflammatory Polyarthritis
Bibliographic record
Abstract
OBJECTIVE: In our region in Quebec, Canada, access to rheumatologists is very limited. Sharing followup of stable patients with their primary care physicians (PCP) could increase access to rheumatologists. In our study, we assessed the feasibility and potential benefits of sharing followup of inflammatory arthritis (IA) patients with their PCP. METHODS: We reviewed the clinical records of 300 patients with peripheral arthritis who presented at our rheumatology outpatient clinic between July and October 2015. We distributed questionnaires to their treating rheumatologist, asking whether a PCP could participate in the followup of the patient and whether there were any factors that would prevent shared followup. We also distributed questionnaires to PCP to assess their level of comfort in participating in the followup care of patients with arthritis. RESULTS: Chart review was completed on 300 patients. There was no treatment modification in 49% of the cases, and 38% of the visits were deemed unnecessary by the attending rheumatologist. We found that 74% of PCP were very interested in sharing the arthritis followup care of their patients. According to PCP, the main barriers to shared followup were treatment with biological agents, active disease, and need for infiltrations. Main organizational barriers were the lack of rheumatologist availability to see patients urgently (46%) and the lack of clear guidelines for the management of IA (58%). CONCLUSION: Up to 38% of peripheral IA visits to a rheumatologist could have been prevented and done by a PCP. In our department, this represented up to 19 followup visits per week that could have been avoided by involving a PCP.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".