Comparison of examination techniques of anterior and posterior compartments of the leg for the diagnosis of deep vein thrombosis: A new examination technique
Bibliographic record
Abstract
OBJECTIVES: Several examination techniques have been described for the diagnosis of leg deep vein thrombosis. These previously described examination techniques aim to detect muscle pain that occurs secondary to increased pressure in the posterior compartment of the leg. However, to the best of our knowledge no studies investigated the frequency of muscle pain on the anterior compartment in patients with leg deep vein thrombosis the objective of this study is to investigate the prevalence of muscle pain in the anterior compartment. METHODS: The patients who were diagnosed with acute deep vein thrombosis were included in this prospective cross-sectional study. Each patient was examined using the techniques that determine the pain on the posterior compartment as well as using the technique we described to detect muscle pain on the anterior compartment. RESULTS: Two hunderd forty three patients were enrolled in the study. Among those, both distal and proximal deep vein thrombosis was present in 128 (52.7%) patients. 75% of them had muscle pain in the anterior compartment. CONCLUSION: The results suggested that examination of muscle pain in anterior compartment of leg in patients with both proximal and distal deep vein thrombosis can be used as an additional physical examination techniques for early diagnosis.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".