Important Outcomes for Type 2 Diabetes Mellitus: The Patient’s Perspective
Bibliographic record
Abstract
Context: Patient Important Outcomes (PIOs) was first introduced in the literature with the criticism that research studies were designed with outcomes relevant to health care providers, but not relevant/important to patients. Although physicians identify treatment outcomes for patients with type 2 diabetes mellitus, patients’ perspective on outcomes important to them also need consideration in patient-centered diabetes care. Objective: To identify important outcomes for type 2 diabetes mellitus care from the perspective of both patients and physicians. Study Design & Analysis: Mixed methods study employing physician survey and patient focus groups. Quantitative descriptive statistics (frequencies) were calculated for physician survey data. Qualitative thematic analysis using constant comparative technique was used for patient focus group data. Setting: Academic family medicine clinic in Edmonton, Canada. Population Studied: Seven physicians and 15 patients took part in the study. English-speaking patients, ≥ 18 years of age with a diagnosis of type 2 diabetes mellitus at least 6 months prior to the study were eligible to participate. Family physicians who practiced at the clinic were eligible to take part. Intervention/Instrument & Outcome Measures: Family physicians were asked to independently rank a list of common diabetes treatment outcomes. Focus groups were conducted with patients using semi-structured questions. Focus group questions addressed patients’ goals/outcomes regarding successful maintenance of diabetes, healthy living with diabetes, and important future outcomes. Results: While, there was overlap between patients and physicians in a number of important outcomes, physicians ranked prevention of micro- and macro-vascular outcomes highest, whereas patients focused more on outcomes related to maintenance of their functional abilities/independence. Moreover, patients commented on the value of having their doctor understand what is important to them. Patients also emphasized the importance of multifaceted, individualized care plans, along with a strong doctor-patient relationship, in managing diabetes and preventing adverse physiological and social outcomes. Conclusion: Differences exist between patient and physicians in important outcomes in diabetes care. It is imperative to the understand the patient’s perspective and to include the patient voice in the delivery of patient-centered diabetes care.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".