Tumors Masquerading as Hematomas
Bibliographic record
Abstract
Suboptimal patient management can occur when malignant soft tissue tumors with internal hemorrhage masquerade as simple hematomas. We retrospectively reviewed 31 patients with malignancies who had diagnostic delays averaging 6.7 months (range, 1.0-49.3 months). The diagnoses included soft tissue sarcomas (27), metastatic cancers (three), and lymphoma (one). History of subcutaneous ecchymosis was positive in only five patients (three of whom had trauma), negative in 18, and unknown in eight. Ecchymosis was present in two patients, absent in 20, and unknown in nine. Previous treatments included observation and reassurance (21), aspiration (11), incision and drainage (10), unplanned resections (seven), physical therapy (seven), medication administration (six), and arthroscopy (one). Interpretations of initial MRI (21) and ultrasound (four) did not raise suspicion of underlying cancers. Traumatic hemorrhage usually causes subcutaneous ecchymosis. However, intratumoral hemorrhage often is contained by a pseudocapsule, which prevents fascial plane tracking and subcutaneous ecchymosis, thus providing a diagnostic clue. Magnetic resonance imaging and ultrasound studies may not accurately diagnose questionable lesions. Diagnostic delay or inappropriate treatment may result if patients do not receive appropriate followup, biopsy (usually open), or referral whenever the diagnosis is in doubt.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".