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Record W3003926216 · doi:10.1155/2020/7568671

Collision Glial Neoplasms Arising in an Ovarian Mature Cystic Teratoma: A Rare Event

2020· article· en· W3003926216 on OpenAlexaff
Abdelrazak Meliti, Bayan Hafiz, Haneen Al‐Maghrabi, Abdulrahim Gari

Bibliographic record

VenueCase Reports in Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPathologyTeratomaNeoplasmGerm cell tumorsOvarian TeratomaMedicinePilocytic astrocytomaNeuroepithelial cellGerm cellImmature teratomaAstrocytomaBiologyEmbryonic stem cellCancer researchGliomaInternal medicine

Abstract

fetched live from OpenAlex

Germ cell neoplasms represent around 20% of all ovarian tumors. They most frequently affect children and young adults. Mature cystic teratoma is a common benign ovarian neoplasm comprising about 95% and is made up of all three germ cell embryonic layers. By definition, mature cystic teratoma may be derived from any of the three germ cell lines. On the other hand, immature teratomas contain primitive neuroepithelial elements. However, it is quite uncommon in the English literature to have a neuroepithelial glial neoplasm arising in a mature cystic teratoma of an adolescent. Interestingly enough, all published cases described a single type of glial neoplasm arising in mature ovarian teratoma. Herein, the authors discuss a unique case of concomitant occurrence of two different glial neoplasms, namely pilocytic astrocytoma and subependymoma arising in an ovarian mature cystic teratoma. To the best of our knowledge, this is the first reported case with such a distinctive histopathologic finding.

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)0.003
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.002
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 head0.018
GPT teacher head0.292
Teacher spread0.275 · 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
Published2020
Admission routes1
Has abstractyes

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