Incidence of deep vein thrombosis in erysipelas or cellulitis of the lower extremities
Bibliographic record
Abstract
The incidence of deep vein thrombosis (DVT) in patients with erysipelas and cellulitis of the lower extremities is unknown. As such, the indication and efficacy of prophylactic anticoagulation for prevention of DVT in these patients is unclear. The main goal of this review is to provide an estimate of the incidence of DVT in erysipelas and cellulitis based on existing literature. A comprehensive search of the electronic sources: MEDLINE, EMBASE, CINAHL, LILAC and Cochrane without any language limitation was performed from 1950 to April 2011 for articles focused on the occurrence of DVT in cellulitis or erysipelas of the lower extremities. The selected studies were divided into two groups according to presence or absence of systematic investigation for DVT. Those studies in which the patients received prophylactic or therapeutic anticoagulants before a diagnosis of DVT were excluded. The reported incidence rate of DVT in patients with erysipelas or cellulitis of the lower extremities is highly variable, ranging from 0 to 15%. In this review, the overall incidence rates of DVT in studies with and without systematic investigation for thromboembolism were 2.72% (95% CI: 1.71-3.75%) and 0.68% (95% CI: 0.27-1.07%), respectively. Given the low reported overall incidence of DVT, neither routine prophylactic anticoagulation nor systematic paraclinical investigation for DVT is indicated in low risk patients with erysipelas or cellulitis of the lower extremities. DVT should still be considered in patients with high pretest probability or other thromboembolic risk factors.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".