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Record W1980623077 · doi:10.1097/dad.0b013e3181914cf1

Cutaneous Solitary Neural Hamartoma: Report of an Unusual Case

2009· article· en· W1980623077 on OpenAlexaff
Ayman Al Habeeb, Hisham Alkhalidi, Halliday Idikio, Danny Ghazarian

Bibliographic record

VenueAmerican Journal of Dermatopathology · 2009
Typearticle
Languageen
FieldMedicine
TopicTumors and Oncological Cases
Canadian institutionsSunnybrook Health Science CentreToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsHamartomaPerineuriumPathologyAnatomyNeurofibromatosisMedicineNodule (geology)DermisHidradenomaBiologyPeripheral nerve

Abstract

fetched live from OpenAlex

Cutaneous hamartomas are tumor-like proliferations of tissue indigenous to the organ but arranged abnormally. There are examples in the literature of cutaneous hamartomas composed of a variety of different components. To our knowledge, there is no previous report of such cutaneous solitary neural hamartoma. Our case occurred in a 51-year-old man with pain and paresthesia in the right shoulder associated with a nodule that was surgically removed. There was no history of trauma, other skin nodules, neurofibromatosis, or tuberous sclerosis. Histologically, there was an unencapsulated nodule, composed of mature nerve bundles noted abnormally high within the papillary dermis, extending to the reticular dermis with periadnexal distribution. Immunohistochemically, the nerve bundles were positive for S-100, including the smaller nerve twigs, and the perineurium was highlighted by epithelial membrane antigen, reminiscent of normal peripheral nerves. Although, neural components including mature nerve bundles have been described in various cutaneous hamartomas, this represents a peculiar case of a cutaneous mature peripheral nerve hamartoma. Whether this is related to other entities of cutaneous hamartomas (ie, neurofollicular hamartoma, folliculosebaceous cystic hamartoma) is not yet apparent, although it is probably a unique entity.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.312
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2009
Admission routes1
Has abstractyes

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