Predictors of a Positive Duplex Scan in Patients with a Clinical Presentation Compatible with Deep Vein Thrombosis or Cellulitis
Bibliographic record
Abstract
BACKGROUND: Deep vein thrombosis (DVT) and cellulitis are common conditions whose symptoms lead patients to seek medical attention in the emergency department (ED). Distinguishing between these two conditions quickly and accurately is important. OBJECTIVES: To determine the yield of duplex scanning among ED patients whose clinical presentation is compatible with DVT or cellulitis. In addition, to determine whether baseline clinical variables are predictive of the final diagnosis among ED patients with an initial clinical impression of 'DVT versus cellulitis' who underwent duplex scanning. METHODS: In this historical cohort study, patients with a final diagnosis of DVT (positive duplex) were compared on several baseline variables with patients with a final diagnosis of cellulitis (negative duplex and antibiotics prescribed) . RESULTS: One hundred-nine of 542 ED patients referred for a duplex scan were initially diagnosed as 'DVT versus cellulitis', 17% of whom had DVT confirmed by a positive duplex scan. Comparing patients with DVT versus those with cellulitis, 0% versus 15.3% had rigors (P=0.06); 0% versus 8.3% had distinct margins of erythema (P<0.01); 5.3% versus 22.2% were currently on antibiotics (P=0.09); and 50% versus 21.3% had an elevated white blood cell count (P=0.04). CONCLUSION: There are differences in a number of baseline characteristics of 'DVT versus cellulitis' patients who went on to have either positive or negative duplex scans, some of which were statistically significant despite the limited sample size. These findings should be confirmed prospectively in a larger study sample since they may have the potential to aid in the clinical differentiation between DVT and cellulitis.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".