The morbidity and mortality following a diagnosis of peripheral arterial disease: Long-term follow-up of a large database
Bibliographic record
Abstract
BACKGROUND: Awareness of the significance of peripheral arterial disease is increasing, but quantitative estimates of the ensuing burden and the impact of other risk factors remains limited. The objective of this study was to fill this need. METHODS: Morbidity and mortality were examined in 16,440 index patients diagnosed with peripheral arterial disease in Saskatchewan, Canada between 1985 and 1995. Medical history and patient characteristics were available retrospectively to January 1980 and follow-up was complete to March 1998. Crude and adjusted event rates were calculated and Kaplan-Meier survival curves estimated. Cox proportional hazards analyses were conducted to examine the effect of risk factors on these rates. Patients suffering a myocardial infarction or ischemic stroke in Saskatchewan provided two reference populations. RESULTS: Half of the index patients were male; the majority was over age 65; 73% had at least one additional risk factor at index diagnosis; 10% suffered a subsequent stroke, another 10% a myocardial infarction, and 49% died within the mean follow-up of 5.9 years. Annual mortality (8.2%) was higher among patients with PAD than after a myocardial infarction (6.3%) but slightly lower than that in patients suffering a stroke (11.3%). Index patients with comorbid disease (e.g., diabetes) were at highest risk of death and other events. CONCLUSION: A diagnosis of peripheral arterial disease is critical evidence of more widespread atherothrombotic disease, with substantial risks of subsequent cardiovascular events and death. Given that the majority has additional comorbidities, these risks are further increased.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".