Acrokeratosis Paraneoplastica (Bazex Syndrome) Presenting in a Patient with Metastatic Breast Carcinoma: Possible Etiologic Role of Zinc
Bibliographic record
Abstract
BACKGROUND: Bazex syndrome (acrokeratosis paraneoplastica) is a rare paraneoplastic syndrome that usually occurs in males over 40 years old and is particularly associated with squamous cell carcinoma of the upper aerodigestive tract and adenopathy above the diaphragm. OBJECTIVE: The objectives of our article are (1) to describe a unique case of acrokeratosis paraneoplastica and (2) to review the current literature regarding skin findings, commonly associated neoplasms, and treatment options relative to this condition. PATIENT: We describe a 68-year-old female with lobular breast carcinoma, complicated by local and distant recurrences, who presented with a 1-year history of prominent acral skin and nail changes. RESULTS: Our patient's clinical skin findings improved significantly following treatment and partial remission of her underlying malignancy. CONCLUSIONS: Our patient represents one of few females described with this syndrome, which is especially rare in association with lobular breast carcinoma. Further, the patient's presentation is unique as she was discovered to demonstrate laboratory findings consistent with coexistent porphyria cutanea tarda and relative zinc deficiency.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| 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".