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Record W2316631237 · doi:10.1097/pgp.0000000000000204

Uterine Leiomyoma With Osteoclast-like Giant Cells

2015· article· en· W2316631237 on OpenAlexaff
Marie‐Christine Guilbert, Vanessa Samouëlian, Kurosh Rahimi

Bibliographic record

VenueInternational Journal of Gynecological Pathology · 2015
Typearticle
Languageen
FieldMedicine
TopicUterine Myomas and Treatments
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsLeiomyomaLeiomyosarcomaUterine leiomyomaSmooth Muscle TumorHysterectomyPathologyMedicineCD68Giant cellSmooth muscleUterusImmunohistochemistryInternal medicine

Abstract

fetched live from OpenAlex

Numerous histologic variants of uterine leiomyomas have been described. The main interest in recognizing these variants is differentiating them from leiomyosarcoma. Osteoclast-like giant cells (OLGC) have been described in association with leiomyosarcoma but to our knowledge, never with leiomyoma. We here report the case of a 58-year-old woman who underwent an elective total hysterectomy with bilateral salpingo-oophorectomy and bilateral pelvic lymphadenectomy for endometrial atypical complex hyperplasia. Multiple typical uterine leiomyoma were identified. One of them showed numerous OLGC admixed with fascicules of bland smooth muscle cells. No atypical features were identified in multiple sections of this otherwise classic uterine leiomyoma. The OLGC showed strong positivity for CD68. The patient, on follow-up, did not show any evidence of recurrent or metastatic disease. This unusual finding expands the morphologic spectrum of uterine leiomyomas. When confronted with a uterine smooth muscle cell tumor with an OLGC component, it is important to search for atypical features diagnostic of leiomyosarcoma.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.030
GPT teacher head0.309
Teacher spread0.279 · 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 designObservational
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

Citations4
Published2015
Admission routes1
Has abstractyes

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