Towards rational approaches of health care utilization in complex patients: an exploratory randomized trial comparing a novel combined clinic to multiple specialty clinics in patients with renal disease-cardiovascular disease-diabetes
Bibliographic record
Abstract
BACKGROUND: Optimal utilization of health care resources for patients with chronic conditions is an increasing focus of health care policy researchers and clinicians. Kidney disease, diabetes (DM) and cardiovascular disease (CVD) often coexist within one individual, but current systems are designed to manage individual conditions. We sought to examine if streamlining medical care of complex patients (two or more conditions) is associated with similar, worse or improved outcomes using a randomized controlled study design. METHODS: Patients attending a kidney care clinic (KCC) and at least one other specialty clinic of interest (DM, CVD) were randomly assigned to either the 'combined clinic (CC)' arm, where resources from all three were integrated into one clinic, or to the 'standard care' arm with continued attendance at multiple specialty clinics (MC), including the KCC. The primary outcome was hospitalization rate and sample size was calculated based on non-inferiority. RESULTS: Of 150 subjects enrolled, 11 subjects exited before study commencement: 139 remained for final analysis. Other than older age in the MC group (P = 0.009), the demographics were comparable. Hospitalization rates were not different (95% CI for the difference: 0.013-0.207; P = 0.03). Similar proportions in each group achieved clinical and laboratory targets. Mortality (13%) and dialysis (32%) rates were the same between groups. Differences in the cost of clinic visits alone were $86,400 per year in favor of the CC. CONCLUSIONS: Medical care of complex patients may be delivered in a single combined specialty clinic as compared to multiple disease specific clinics without compromising patient care or important health outcomes, with demonstrable outpatient costs savings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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".