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Record W1991351790 · doi:10.1155/2012/212746

Metastatic Parotid Myoepithelial Carcinoma in a 7-Year-Old Boy

2012· article· en· W1991351790 on OpenAlexaff
Issam Saliba, Nazir El Khatib, Antoine Nehme, Selim Nasser, Nabil Moukarzel

Bibliographic record

VenueCase Reports in Pediatrics · 2012
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineParotid glandMyoepithelial cellPathologyCytokeratinParotidectomyFacial nerveNeck dissectionMetastasisMalignancyCervical lymph nodesCarcinomaImmunohistochemistryCancerInternal medicine

Abstract

fetched live from OpenAlex

Myoepithelial carcinoma is a rare malignancy of the parotid gland that is usually seen in adults. We report the first case in children of myoepithelial carcinoma of the parotid gland with massive invasion of the facial nerve and metastasis to cervical lymph nodes. Due to its rarity, the treatment and the clinical course of this tumor are not well defined yet. We performed a total parotidectomy, a modified neck dissection, and a postoperative radiotherapy in 7-year-old boy. Sparing of the facial nerve was impossible; it was sacrificed and grafted with a sural nerve. Histopathology confirmed the diagnosis of a parotid gland carcinoma and immunohistochemical markers showed that the tumor cells express cytokeratin, epithelial membrane antigen, cytokeratin 7, smooth muscle actin, P63, CEA, and S100. This pattern of immunostaining is consistent with the diagnosis of myoepithelial carcinoma. On the postoperative tenth month he presented with a pulmonary and lumbar vertebra metastasis.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.291
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2012
Admission routes1
Has abstractyes

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