Does the Clinical Examination Predict Lower Extremity Peripheral Arterial Disease?
Bibliographic record
Abstract
CONTEXT: Lower extremity peripheral arterial disease (PAD) is common and associated with significant increases in morbidity and mortality. Physicians typically depend on the clinical examination to identify patients who need further diagnostic testing. OBJECTIVE: To systematically review the accuracy and precision of the clinical examination for PAD. DATA SOURCES, STUDY SELECTION, AND DATA EXTRACTION: MEDLINE (January 1966 to March 2005) and Cochrane databases were searched for articles on the diagnosis of PAD based on physical examination published in the English language. Included studies compared an element of the history or physical examination with a reference standard of ankle-brachial index, duplex sonography, or angiogram. Seventeen of the 51 potential articles identified met inclusion criteria. Two of the authors independently extracted data, performed quality review, and used consensus to resolve any discrepancies. DATA SYNTHESIS: For asymptomatic patients, the most useful clinical findings to diagnose PAD are the presence of claudication (likelihood ratio [LR], 3.30; 95% confidence interval [CI], 2.30-4.80), femoral bruit (LR, 4.80; 95% CI, 2.40-9.50), or any pulse abnormality (LR, 3.10; 95% CI, 1.40-6.60). While none of the clinical examination features help to lower the likelihood of any degree of PAD, the absence of claudication or the presence of normal pulses decreases the likelihood of moderate to severe disease. When considering patients who are symptomatic with leg complaints, the most useful clinical findings are the presence of cool skin (LR, 5.90; 95% CI, 4.10-8.60), the presence of at least 1 bruit (LR, 5.60; 95% CI, 4.70-6.70), or any palpable pulse abnormality (LR, 4.70; 95% CI, 2.20-9.90). The absence of any bruits (iliac, femoral, or popliteal) (LR, 0.39; 95% CI, 0.34-0.45) or pulse abnormality (LR, 0.38; 95% CI, 0.23-0.64) reduces the likelihood of PAD. Combinations of physical examination findings do not increase the likelihood of PAD beyond that of individual clinical findings. However, when combinations of clinical findings are all normal, the likelihood of disease is lower than when individual symptoms or signs are normal. A PAD scoring system, which includes auscultation of arterial components by handheld Doppler, provides greater diagnostic accuracy. CONCLUSIONS: Clinical examination findings must be used in the context of the pretest probability because they are not independently sufficient to include or exclude a diagnosis of PAD with certainty. The PAD screening score using the hand-held Doppler has the greatest diagnostic accuracy.
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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 | 0.017 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".