Shared medical appointments for patients with a nondiabetic physical chronic illness: A systematic review
Bibliographic record
Abstract
OBJECTIVES: Shared medical appointments are group appointments, with an optional individual consultation, for patients diagnosed with chronic illnesses. Shared medical appointments improve diabetes management, but little is known about their use for other illnesses. The objective was to determine the effect that shared medical appointments have on patients with a physical chronic illness, healthcare providers, and the healthcare system. METHODS: A systematic review was conducted searching databases from January 1970 to September 2016. Eligible trials evaluated shared medical appointments for patients with a homogeneous chronic illness, excluding diabetes and mental illness. Screening, data extraction, and risk of bias were conducted independently by two authors. Analysis was descriptive. RESULTS: Of 2364 citations, nine randomized trials were included. Shared medical appointments were evaluated for cardiovascular illnesses (four studies), breast cancer, chronic kidney disease, Parkinson's disease, stress urinary incontinence, and carpal tunnel syndrome. Compared to usual care, no negative effects on patient quality of life, knowledge and satisfaction were reported. One study reported no difference in healthcare provider satisfaction. Another study showed fewer hospital admissions for patients who attended shared medical appointments. DISCUSSION: Few rigorous studies evaluated the use of shared medical appointments for chronic illnesses. Overall, there appears to be no patient harms. Further studies should include more objective outcomes and larger sample sizes.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".