Scalp Leiomyosarcoma: Diagnosis and Treatment During a Global Pandemic With COVID-19
Bibliographic record
Abstract
Leiomyosarcoma is an aggressive, uncommon sarcoma effecting smooth muscle tissue. Prompt tissue diagnosis and staging workup are keys to preventing distant metastasis. Identification of this rare sarcoma has become increasingly difficult with decreased ability to seek out non-coronavirus disease 2019 (COVID-19) medical care. The pandemic has caused a widespread healthcare demand with providers reaching their full capacity causing care and resources to be shifted to the pandemic. We have experienced an 83-year-old male who significantly delayed to seek any medical attention for his scalp lesion for several months due to a combination of fear and decreased available appointments. Since the patient presented with a delayed scalp leiomyosarcoma, he required an extensive excision and flap reconstruction for the lesion. This case sheds light on the importance of weighing the risks and benefits associated with cancer management during the pandemic for both patients and healthcare providers. The healthcare system's response to the pandemic also played a role in this case as well, with shorter appointment times and decreased frequency of follow-up. As a result, the pandemic has had a catastrophic impact on the diagnostic pathway for cancer. This case report discusses the difficulties in diagnosing and treating a rare cancer such as scalp leiomyosarcoma amidst the global pandemic and the importance of telemedicine in improving future outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".