A scoping Review of tools used to assess patient Complexity in rheumatic disease
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatic diseases often have multiple comorbidities which may impact well-being leading to high psychosocial complexity. This scoping review was undertaken to identify complexity measures/tools used in rheumatology that could help in planning and coordinating care. METHODS: MEDLINE, EMBASE and CINAHL were searched from database inception to 14 December 2019 using keywords and Medical Subject Headings for "care coordination", "complexity" and selected rheumatic diseases and known complexity measures/tools. Articles describing the development or use of complexity measures/tools in patients with adult rheumatologic diagnoses were included regardless of study design. Included articles were evaluated for risk of bias where applicable. RESULTS: The search yielded 407 articles, 37 underwent full-text review and 2 were identified during a hand search with 9 included articles. Only 2 complexity tools used in populations of adult patients with rheumatic disease were identified: the SLENQ and the INTERMED. The SLENQ is a 97-item patient needs questionnaire developed for patients with systemic lupus (n = 1 study describing tool development) and applied in 5 cross-sectional studies. Three studies (a practice article, trial and a cross-sectional study) applied the INTERMED, a clinical interview to ascertain complexity and support coordinated care, in patients with rheumatologic diagnoses. CONCLUSIONS: There is limited information on the use of patient complexity measures/tools in rheumatology. Such tools could be applied to coordinate multidisciplinary care and improve patient experience and outcomes. PATIENT CONTRIBUTION: This scoping review will be presented to patient research partners involved in co-designing a future study on patient complexity in rheumatic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".