Successful use of platelet-rich plasma as a regeneration technique for a non-healing diabetic leg ulcer with chronic osteomyelitis: A case report
Bibliographic record
Abstract
Chronic diabetic leg ulcers can be challenging to manage, particularly when osteomyelitis coexists resulting in serious or even life-threatening conditions if not managed appropriately. Such cases require very long periods of treatment with several debridements, antibiotic bead implants, skin grafts or even amputations in many unfortunate cases. Despite that, conventional management appears to be inadequate with poor outcomes and a higher relapse rate. We report a case of a 26-year-old type 1 diabetic female patient presented with a history of non-healing diabetic leg ulcer complicated by chronic osteomyelitis of the left leg. Several wound debridements were performed with the application of vacuum-assisted closure, and culture-guided antibiotics were given. However, only minimal improvements were observed. Eventually, the leg was labeled as an unsalvagable limb due to poor blood supply based on an ankle-brachial index. Platelet-rich plasma (PRP) was tried showing promising results and significant improvement as confirmed by a gallium scan, which showed evidence of osteomyelitis remission. Two months later, the patient improved significantly with the ulcer healed and the osteomyelitis resolved. Autologous PRP promotes wound healing by providing necessary growth factors, reducing inflammation, and showing bactericidal activity. All efforts must be taken to preserve any limb before labeling it non-salvageable. This case report aimed to draw attention to the method that can be implemented to manage non-healing diabetic leg ulcers complicated by chronic osteomyelitis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".