The Influence of Diabetes Mellitus on Mortality of Patients After Lower Extremity Amputation: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: The influence of diabetes mellitus (DM) on mortality following lower extremity amputation (LEA) remains controversial. This systematic review and meta-analysis aimed to determine the influence of DM on long-term mortality (LTM) and short-term mortality (STM) after amputation. MATERIALS AND METHODS: The Medline, the Cochrane library, and Embase databases were searched. The primary and secondary outcomes were LTM and STM following amputation. One-year and 30-day all-cause mortality after amputation were considered as LTM and STM, respectively. A random-effects model was utilized to pool results. To evaluate the stability of results, subgroup analyses and sensitivity analyses were conducted. RESULTS: Twenty-three cohort studies with a total of 58,219 patients were included, among which 31,750 (54.5%) patients had DM. The mean score of included studies evaluated by Newcastle-Ottawa Scale was 7.65, indicating moderate to high quality. The pooled results showed no significant difference in 1-year LTM (risk ratio [RR], 0.96; 95% CI 0.86-1.07) after amputation. However, 3-year (RR, 1.22; 95% CI 1.01-1.47) and 5-year (RR, 1.18; 95% CI 1.07-1.31) LTMs of DM patients were obviously higher than that of NDM (non-diabetes mellitus) patients. The STM of the DM group was significantly lower than the NDM group (RR, 0.80; 95% CI 0.64-0.98). CONCLUSIONS: The current study revealed that DM patients had an obvious lower STM following LEA, but the risk of DM on LTM after amputation was gradually increased with time. More attention should be paid to the long-term survival of DM patients after LEA.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".