PIII-15Prioritizing treatment preferences in patients with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND The objective of this study was to identify how type 2 diabetic patients prioritize specific treatment and management options based upon their personal values. METHODS 24 patients were recruited from a diabetes clinic. Using information from a prior qualitative study on diabetes management a questionnaire was constructed composed of 14 statements relating to management options. Patients indicated the importance of each statement on a 10-point visual analog scale and a factor analysis was conducted. RESULTS The most important issue for patients was obtaining a treatment that provides the best possible blood glucose levels (mean=9.1; SD=0.7); treatment with alternative remedies was the least important issue (mean=4.1; SD=3.4). Diet & exercise ranked 3rd, pills and insulin ranked 12th and 13th respectively. The factor analysis demonstrated a correlation structure that generated 5 themes (lifestyle, knowledge, effectiveness, autonomy, efficiency). No demographic characteristics predicted the statement ratings. CONCLUSIONS The study has demonstrated that diabetes patients do set priorities for their care. The results have implications for how health care providers might communicate therapeutic options, particularly with a the need to link management to goals. Because inter-patient responses varied greatly there may also be a need to develop treatment strategies that are consistent with individualized preferences rather than generalizing from average patient values. Clinical Pharmacology & Therapeutics (2005) 79, P62–P62; doi: 10.1016/j.clpt.2005.12.223
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".