Complex wounds tend to develop more rapidly in patients receiving hemodialysis because of diabetes mellitus
Bibliographic record
Abstract
The number of patients requiring dialysis because of diabetes mellitus is increasing and such patients often have complex chronic wounds, which are difficult to heal. However, there are few retrospective studies of wounds requiring surgical treatment. We evaluated 14 patients receiving hemodialysis (HD) (8 because of diabetes and 6 because of other diseases) who had extremity wounds and underwent surgical treatment in our unit from 2004 through 2007. We investigated differences in the cause of wounds, and in the interval between the start of HD and wound development. Wounds in patients undergoing HD because of diabetes originated due to ischemia in 2 cases (25%), trauma in 2 cases (25%), and infection in 4 cases (50%). Seven of 8 wounds developed infection with methicillin-resistant Staphylococcus aureus (MRSA). Wounds in patients undergoing HD because of other diseases developed due to ischemia in 2 cases (33%) and trauma in 4 cases (67%). Three of 6 wounds developed infection and MRSA were isolated from 2 wounds. The interval between the start of HD and wound development was significantly shorter in patients with diabetes than in patients without diabetes. All patients with infectious wounds required immediate debridement. We conclude that patients receiving HD because of diabetes are likely to have more severe and rapidly developing wounds due to infections. Thus, they usually require immediate debridement before blood access shunt infection occurs.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".