Multiple Venous Thromboses Presenting as Mechanical Low Back Pain in an 18-Year-Old Woman
Bibliographic record
Abstract
OBJECTIVE: The purpose of this case report is to describe a patient who presented with acute musculoskeletal symptoms but was later diagnosed with multiple deep vein thrombosis (DVT). CLINICAL FEATURES: An 18-year-old female presented to a chiropractic clinic with left lumbosacral pain with referral into the posterior left thigh. A provisional diagnosis was made of acute myofascial syndrome of the left piriformis and gluteus medius muscles. The patient received 3 chiropractic treatments over 1 week resulting in 80% improvement in pain intensity. Two days later, a sudden onset of severe abdominal pain caused the patient to seek urgent medical attention. A diagnostic ultrasound of the abdomen and pelvis were performed and interpreted as normal. Following this, the patient reported increased pain in her left leg. Evaluation revealed edema of the left calf and decreased left lower limb sensation. A venous Doppler ultrasound was ordered. INTERVENTION AND OUTCOMES: Doppler ultrasound revealed reduction of the venous flow in the femoral vein area. An additional ultrasonography evaluation revealed an extensive DVTs affecting the left femoral vein and iliac axis extending towards the vena cava. Upon follow-up with a hematologist, the potential diagnosis of May-Thurner syndrome was considered based on the absence of blood dyscrasias and sustained anatomical changes found in the left common iliac vein at its junction with the right common iliac artery. A week following discharge, she presented with chest pain and was diagnosed with venous thromboembolism. The patient was successfully treated with anticoagulation therapy and insertion of a vena cava filter. CONCLUSION: Although DVTs are common in the general population, presence in low-risk individuals may be overlooked. In the presence of subtle initial clinical signs such as those described in this case report, clinicians should keep a high index of suspicion for a DVT. Rapid identification of such clinical signs in association with a lack of objective examination findings warrants further evaluation due to potentially negative outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".