Self-reported health condition severity and ambulation status postmajor dysvascular limb loss
Bibliographic record
Abstract
BACKGROUND: Individuals with dysvascular lower limb amputations (LLA) secondary to complications of peripheral arterial disease (PAD) and/or diabetes have high rates of co-morbidities. OBJECTIVES: To describe self-reported health condition severity and their association with sociodemographic factors and ambulations status among individuals with major dysvascular LLA. STUDY DESIGN: Cross sectional telephone and in person survey with adults with major dysvascular LLA living in the community setting in Ontario, Canada. METHODS: Survey by phone/in person, and completion of the Dysvascular Conditions Scale and Special Interest in Amputee Medicine Mobility (SIGAM) Grade by each participant. RESULTS: Two hundred thirty-one individuals with major dysvascular LLAs participated in the study. Most of them were male individuals (80.5%) and had undergone a transtibial amputation (74%). On average, participants were 3.4 years postlimb loss and had five identified Dysvascular Conditions Scale health conditions. The top five reported health conditions were diabetes, hypertension, phantom limb pain, musculoskeletal pain, and back pain. With the exclusion of hypertension, these conditions were also perceived by respondents to be quite severe for their impact. Vision impairment was also rated as being severe in nature. Lower mobility Special Interest Group in Amputee Medicine grades were associated with higher health condition severity scores. CONCLUSIONS: Individuals with dysvascular limb loss experience high multimorbidity with perceived negative impact on their overall wellness and function. Rehabilitation and self-management strategies to help patients with dysvascular LLAs to manage chronic health conditions may improve outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".