Melanoma Identification and Management in an Unsheltered Male Using Teledermatology: Street Medicine Perspective
Bibliographic record
Abstract
Skin cancers are concerning for unsheltered people experiencing homelessness because of their high levels of sun exposure. Currently, there is little data on the prevalence of skin cancers in people experiencing homelessness. Skin diseases are often untreated in people experiencing homelessness due to a lack of access to specialized care. Miami Street Medicine (MSM) is an organization that provides people experiencing homelessness in the Miami Health District with medical care in a nonclinical street setting, near overpasses, sidewalks, and encampments. We present a case of an unsheltered 59-year-old male with a pigmented, 2 cm × 2 cm facial lesion that developed over several years. Through a teledermatology consultation, his lesion was highly suspicious of melanoma and further evaluation was recommended. Due to a lack of insurance, he could not be treated at any dermatology clinic. Coincidentally, 2 weeks later, he developed cellulitis of his lower extremity and was admitted to the local safety-net hospital through the emergency department. By coordinating with his primary inpatient team, MSM was able to include a biopsy of the lesion as part of his hospital stay. The results demonstrated melanoma in situ. The vital course of action was to ensure treatment before metastasis. After registration for insurance and follow-up with a surgical oncology team, he is weeks away from excision and reconstruction surgery. His unsheltered status made follow-up difficult, but MSM bridged the gap from the street to the clinical setting by incorporating teledermatology into patient evaluations and leveraging connections with community shareholders such as charitable clinics and volunteer physicians. This case also represents the barriers to care for cancer-based dermatologic outreach among people experiencing homelessness.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".