Health Utility of Patients with Non-Healing Diabetic Foot Ulcers
Bibliographic record
Abstract
Diabetic foot ulcers (DFUs) impart a large burden on patients and the healthcare system in Canada. Health utility estimates are an integral part of determining the cost-effectiveness of treatments for DFUs. The objective of this thesis was to identify health utility estimates for patients with non-healing DFUs. A systematic review of studies reporting health utility estimates for non-healing DFUs was conducted and included nine studies. The quality of the studies, as it related to the health utility estimates for non-healing DFUs, was difficult to determine due to a lack of reporting of study and patient characteristics. The health utility estimates ranged from 0.44 to 0.89. None of the studies investigated for factors associated with the health utility of patients with non-healing DFUs. In addition, an exploratory regression analysis of data from a randomized controlled trial (RCT) of hyperbaric oxygen therapy (HBOT) in patients with chronic, non-healing DFUs was conducted. No factors were identified that were associated with health utility; however, the sample size was small and the analysis exploratory. Further research is required to identify such factors. Finally, a descriptive regression model, including several baseline factors, was created which provided a heath utility estimate of 0.647 for Canadian patients with non-healing DFUs; however, the results should be interpreted with caution, especially as some subgroups had very small numbers of patients (e.g., Wagner Grade of 4; patients with 4 or more wounds). In summary, guidance is lacking on the best methodology to conduct and analyze studies that provide estimates of the health utility of patients with non-healing DFUs, or any other health state, that are to be used to inform economic evaluations. Additionally, a tool is needed to aid analysts in critically appraising studies so that they can select the best estimate of health utility value to include in economic evaluations.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".