Diabetes‐Associated Major Limb Amputation in Solomon Islands: A National, 5‐Year Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Solomon Islands is a Western Pacific nation with one of the highest diabetes prevalences in the world. The delivery of surgical care is challenging given the country's complex geography and limited healthcare resources. This retrospective study aims to quantify diabetes-associated major limb amputation and to describe the characteristics of patients undergoing this procedure. METHODS: Demographic, clinical, and surgical data were abstracted for patients who underwent major limb amputation secondary to diabetic infection. Summary statistics were gathered from this dataset. Additionally, patients' village names were abstracted, located from publicly available data, and mapped using ArcPro software. The geodesic distance between villages and the surgical centers performing amputation was calculated. Univariate and multivariate modeling was employed to assess the relationship between an a priori set of variables and several outcomes. RESULTS: Over a 5-year period (2018-2023), 401 amputations were performed on 356 patients with diabetic limb infections. Three hundred and five patients had medical records available for abstraction. The median age was 55 (range: 22-84 years), and 160 were male (52.5%). Trauma was the most common cause of ulceration (N = 135, 44.3%). Delayed presentation was common with 100 patients (32.8%) presenting more than 30 days after symptom onset. The mean Wagner score on presentation was 3.6 (STD: 0.8, range: 2-5). Most patients (N = 293, 96%) experienced surgical delays. Among all patients who underwent major limb amputation, 38 (10.7%) died prior to discharge. The mean linear distance between patient villages and surgical centers was 53.6 km. Greater distance was associated with more delayed presentation (Correlation Coeff: 0.14, p = 0.03) and higher Wagner scores (Kruskal-Wallis statistic: 10.7, p = 0.01). Multivariable logistic regression indicated that males were more likely to undergo above-knee amputation as compared to females (OR: 2.13, p = 0.009). Further multivariable modeling demonstrated that male sex was correlated with higher wait times from presentation to amputation (p = 0.035). CONCLUSION: Major limb amputation is a costly and radical procedure which contributes to severe disability in the local context. Upstream interventions to manage diabetes and facilitate access to surgical care are likely to prevent limb loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".