The predictive value of cumulative plantar tissue stress on future plantar foot ulceration in people with diabetes—A 12‐month prospective observational study
Bibliographic record
Abstract
Abstract Aims Plantar foot ulcers are a burdensome complication of diabetes caused by abnormal foot biomechanics. Predicting foot ulcers aids in their prevention, but the value of peak pressure—the most used biomechanical parameter—is only moderate. We aimed to improve prediction based on the more comprehensive load measure cumulative plantar tissue stress (CPTS). Methods We prospectively observed 60 participants with diabetes at high foot ulcer risk for 12 months. At baseline, we assessed demographic and clinical characteristics—including plantar pre‐ulcers (i.e., abundant callus, haemorrhage, blister, fissure)—and measured barefoot and in‐shoe plantar pressures during walking and standing. Daily‐life weight‐bearing activity and adherence to prescribed footwear were assessed over 7 days after baseline. The primary outcome was plantar foot ulceration during the 12‐month follow‐up. CPTS was calculated (in GPa.s/day) from the above foot‐loading factors and analysed for predicting foot ulcers and its association with pre‐ulcers, using multivariate regression analyses. Results Twenty‐two participants (37%) developed a plantar forefoot ulcer. CPTS was not a significant predictor (odds ratio (OR) = 0.90 (95% confidence interval (CI): 0.50–1.59)) but pre‐ulcers at baseline (OR = 9.97, 95%CI: 1.41–70.65) and walking speed (in m/s) (OR = 0.01, 95%CI: 0.00–0.32) were. CPTS was significantly associated with pre‐ulcers (OR = 2.38, 95%CI: 1.02–5.54). Conclusions CPTS did not predict plantar foot ulceration in our high‐risk participants, but our findings support the mechanical pathway of plantar foot ulceration through pre‐ulcer development and indicate lower walking speed as an important predictor. Assessing walking speed and early identifying and treating pre‐ulcers will help predict and prevent plantar foot ulcers in high‐risk people with diabetes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".