Development of a Rheumatology-specific Patient Concerns Inventory and Its Use in the Rheumatology Outpatient Clinic Setting
Bibliographic record
Abstract
OBJECTIVE: Successful management of rheumatic conditions involves increasing complexity of care. Delivering this in a holistic way is a growing challenge. The aim of our study was to develop a Patient Concerns Inventory (PCI) and assess it in the rheumatology clinic setting. METHODS: This observational exploratory study occurred with 2 phases. In phase I, the PCI was developed after a systematic literature search, expert opinion, and 3 patient focus group discussions. In phase II, the PCI was piloted in a general rheumatology clinic. RESULTS: Fifty-four patients were assessed in the pre-PCI group and 51 in the post-PCI group. Median (IQR) duration of consultation was 8 min (5-14) without PCI and 15 min (10-20) with PCI. The pre-PCI group raised 335 concerns from 50 patients, median (IQR) of 5 (3-10) per patient, rising post-PCI to 521 concerns, median (IQR) of 9 (5-16) from 51 patients, p = 0.002. Additional concerns predominantly arose from "physical and functional well-being" and "social care and well-being" domains. Most patients rated their experience with their doctor in the consultation as excellent or outstanding across all 11 questions in the questionnaire, both before and after the introduction of the PCI to the clinic setting. CONCLUSION: The PCI is a useful holistic needs assessment tool for rheumatology clinics. Although its use may initially prolong the consultation slightly, patients can raise a significantly higher number of concerns, which does not occur at the expense of patient satisfaction. This may help in identifying areas of unmet needs that previously went unnoticed.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".