Traumatic deep vein thrombosis in a soccer player: A case study.
Bibliographic record
Abstract
A 42 year-old male former semi-professional soccer player sustained a right lower extremity popliteal contusion during a soccer game. He was clinically diagnosed with a possible traumatic deep vein thrombosis (DVT), and sent for confirmatory tests. A duplex doppler ultrasound was positive for DVT, and the patient was admitted to hospital for anticoagulation (unfractionated heparin, warfarin). Upon discharge from hospital the patient continued oral warfarin anticoagulation (six months), and the use of compression stockings (nine months). He followed up with his family doctor at regular intervals for serial coagulation measurements, and ultrasound examinations. The patient's only identified major thrombotic risk factor was the traumatic injury. One year after the initial deep vein thrombosis (DVT) the patient returned to contact sport, however he continued to have intermittent symptoms of right lower leg pain and right knee effusion.Athletes can develop vascular injuries in a variety of contact and non-contact sports. Trauma is one of the most common causes of lower extremity deep vein thrombosis (DVT), however athletic injuries involving lower extremity traumatic DVT are seldom reported. This diagnosis and the associated risk factors must be considered during the initial physical examination. The primary method of radiological diagnosis of lower extremity DVT is a complete bilateral duplex sonography, which can be augmented by other methods such as evidence-based risk factor analysis. Antithrombotic medication is the current standard of treatment for DVT. Acute thrombolytic treatment has demonstrated an improved therapeutic efficacy, and a decrease in post-DVT symptoms.There is a lack of scientific literature concerning the return to sport protocol following a DVT event. Athletic individuals who desire to return to sport after a DVT need to be fully informed about their treatment and risk of reoccurrence, so that appropriate decisions can be made.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".