Trends in Initial Lower Extremity Amputation Rates Among Veterans Health Administration Health Care System Users From 2000 to 2004
Bibliographic record
Abstract
OBJECTIVE: To evaluate temporal trends in rates of initial lower extremity amputation (ILEA) among patients with diabetes in the Veterans Health Administration (VHA). RESEARCH DESIGN AND METHODS: Retrospective administrative data analysis of VHA clinic users with diabetes in fiscal years (FY) 2000 to 2004 (1 October 1999-30 September 2004). We calculated annual age- and sex-standardized rates of initial major, minor, and total amputations for the overall population and for various racial/ethnic groups (African Americans, Hispanics, and whites). Trends in ILEA risk were evaluated with and without adjustment for demographic characteristics and other potential risk factors, including presence of microvascular and macrovascular diseases, and antiglycemic treatment. RESULTS: Study populations of VHA patients with diabetes and without prior amputations ranged from 405,580 in FY 2000 to 739,377 in FY 2004. Age- and sex-standardized ILEA rates decreased by 34% (7.08/1,000 patients in FY 2000 to 4.65/1,000 patients in FY 2005) during the 5-year period. Minor and major amputation rates decreased by 33% (4.59 to 3.06/1,000) and 36% (2.49 to 1.59/1,000), respectively. Of major amputations, below-knee rates decreased from 1.08 to 0.87/1,000 (-19%), and above-knee decreased from 1.41 to 0.72/1,000 (-49%). Similar trends were seen for all racial groups. ILEA risk decreased by 28% (odds ratio 0.72 [95% CI 0.68-0.75]) when FY 2004 was compared with FY 2000 in the model, adjusting for demographic characteristics. This risk decrease was 22% in the model adjusting for all independent variables (odds ratio 0.78 [95% CI 0.74-0.82]). CONCLUSIONS: Downward 5-year trends in ILEA rates were observed for all amputation levels and among all racial groups, even after adjustment for risk differences over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".