Podiatrist care and outcomes for patients with diabetes and foot ulcer
Bibliographic record
Abstract
We examined whether outcomes of care (amputation and hospitalisation) among patients with diabetes and foot ulcer differ between those who received pre-ulcer care from podiatrists and those who did not. Adult patients with diabetes and a diagnosis of a diabetic foot ulcer were found in the MarketScan Databases, 2005-2008. Multivariate Cox proportional hazard models estimated the hazard of amputation and hospitalisation. Logistic regression estimated the likelihood of these events. Propensity score weighting and regression adjustment were used to adjust for potentially different characteristics of patients who did and did not receive podiatric care. The sample included 27 545 patients aged greater than 65+ years (Medicare-eligible patients with employer-sponsored supplemental insurance) and 20 208 patients aged lesser than 65 years (non Medicare-eligible commercially insured patients). Care by podiatrists in the year prior to a diabetic foot ulcer was associated with a lower hazard of lower extremity amputation, major amputation and hospitalisations in both non Medicare-eligible commercially insured and Medicare-eligible patient populations. Systematic differences between patients with diabetes and foot ulcer, receiving and not receiving care from podiatrists were also observed; specifically, patients with diabetes receiving care from podiatrists tend to be older and sicker.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".