Development and Evaluation of Patient‐Centered Software for a Weight‐Management Clinic
Bibliographic record
Abstract
OBJECTIVE: To describe a weight-management clinic software system and to report on its preliminary evaluation. RESEARCH METHODS AND PROCEDURES: The software system standardizes the collection of relevant patient information from an initial medical assessment, weekly clinic visits, and laboratory testing protocol of a medically supervised proprietary meal-replacement program in a university-based referral clinic. It then generates monthly patient feedback reports with graphs of clinical and laboratory parameters to support a patient-centered approach to weight management. After patients and clinic physicians review the data to ensure accuracy, the database is used for subsequent patient feedback reports, reports to referring physicians, quality assurance, and research. Clinic physicians and referring physicians were asked to rate their acceptance of the system. In addition, in a retrospective analysis of data generated by the system, outcomes for patients who received system-generated feedback (n = 620) were compared with those who participated in the program before the introduction of feedback (n = 130). RESULTS: Clinic and referring physicians reported that they had high overall satisfaction with the software and that the system saved them time, and the latter group reported that it decreased laboratory use. Regarding patients, the feedback group had lower dropout rates in the latter half of the program, better rates of attendance, completion of laboratory tests, and weight loss after 8 weeks. DISCUSSION: The software seems to facilitate the effectiveness of the treatment protocol for obesity and generates a high-quality database for patient care, clinic administration, quality assurance, and research purposes.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".