Development of a Prognostic Model for Stump Healing in Major Lower Limb Amputation among the Diabetic Population
Bibliographic record
Abstract
Background: This study aimed to explore the effect of haematological markers as well as patient characteristics on stump healing in patients who underwent a lower limb amputation procedure. In addition, a practical model regarding factors that affected stump healing was developed. Methods: Patients who underwent a major lower limb amputation (above knee and below knee) at the Royal Infirmary of Edinburgh from the period of 2007 to 2010 were included in this study. A prognostic model utilizing backward stepwise logistical regression was developed to measure the probability of lower limb stump healing. The relationship between the dependent and independent variables was identified using univariate and multivariate logistic regression. Results: Three variables, namely serum sodium, serum creatinine and serum high density lipid cholesterol were identified which influenced stump healing. Patients with normal serum sodium were 75% more likely to have lower limb stump healing compared to that of patients with abnormal serum sodium (odds ratio [OR] 1.756; 95% confidence interval [CI] 1.048–2.942). Patients with normal serum creatinine were 66% more likely to have their stump healed (OR 1.664; 95% CI 0.94 to 2.946). The healing rate of patients with a normal level of serum high density lipid cholesterol was 75%, in contrast to patients with an aberrant level of serum high density lipids cholesterol (OR 1.753; 95% CI 1.061 to 2.895). The effectiveness of the retrospective stump-healing model was demonstrated by the area under the ROC curve (0.612), which was supported by the Hosmer and Lemeshow goodness-of-fit test (p = 0.879). Conclusions: Serum sodium, serum high density lipid cholesterol and serum creatinine have a strong correlation with lower limb stump healing. However, serum sodium and serum high density lipid cholesterol secondary to multiple co-morbidities in this cohort group could be altered secondary to disease pathology itself.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".