Bibliographic record
Abstract
Melanoma is an aggressive but easily preventable cancer. However, it may have a highly atypical presentation which makes early detection more difficult. This case report discusses a 25-year-old patient with a rare case of melanoma developing underneath the nail of the first toe. The case was originally diagnosed as trauma due to its rarity and epidemiological unlikeliness, however through the patient’s persistence for alternative opinions the correct diagnosis was eventually made. However, this led to an amputation as well as more intense, invasive treatment. There were several points on history (duration of the lesion, appearance of the lesion, and lack of healing progress) which when combined with the lesion’s physical appearance should have made such a presentation suspicious for a more malignant cause. This case highlights the importance of early detection in the prognosis and treatment of patients with cancer, the importance of considering all aspects of a history and physical exam, and the importance of listening to and addressing a patient’s concerns. As always, more common diagnoses should be first considered, but when the story does not match up with the presentation, one should move past the horses to consider the zebras.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".