Patient-Reported Outcome Measures and Risk Factors in a Quality Registry: A Basis for More Patient-Centered Diabetes Care in Sweden
Bibliographic record
Abstract
Diabetes is one of the chronic diseases that constitute the greatest disease burden in the world. The Swedish National Diabetes Register is an essential part of the diabetes care system. Currently it mainly records clinical outcomes, but here we describe how it has started to collect patient-reported outcome measures, complementing the standard registry data on clinical outcomes as a basis for evaluating diabetes care. Our aims were to develop a questionnaire to measure patient abilities and judgments of their experience of diabetes care, to describe a Swedish diabetes patient sample in terms of their abilities, judgments, and risk factors, and to characterize groups of patients with a need for improvement. Patient abilities and judgments were estimated using item response theory. Analyzing them together with standard risk factors for diabetes comorbidities showed that the different types of data describe different aspects of a patient's situation. These aspects occasionally overlap, but not in any particularly useful way. They both provide important information to decision makers, and neither is necessarily more relevant than the other. Both should therefore be considered, to achieve a more complete evaluation of diabetes care and to promote person-centered 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 | 0.284 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".