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O-Methylguanine-DNA Methyltransferase Immunoexpression in a Double Pituitary Adenoma

2010· article· en· W2074498213 on OpenAlexafffund
Safraz Mohammed, Michael D. Cusimano, Bernd W. Scheithauer, Fabio Rotondo, Éva Horváth, Kálmán Kovács

Bibliographic record

VenueNeurosurgery · 2010
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersJarislowsky Foundation
KeywordsMedicinePituitary adenomaMethyltransferaseAdenomaDNACancer researchInternal medicineGeneticsMethylation

Abstract

fetched live from OpenAlex

OBJECTIVE: Double pituitary adenomas in surgical cases are rarely reported. The incidence in published surgical specimens ranges from 0.4% to 1.3%. We present a treatment dilemma of a double adenoma that had differential O-methylguanine-DNA methyltransferase (MGMT) reactivity. CLINICAL PRESENTATION: A 48-year-old man presented with acromegaly and a recurrent pituitary adenoma. He had elevated growth hormone (GH) and elevated insulin-like growth factor blood levels and hyperprolactinemia. INTERVENTION: Subtotal transsphenoidal resection was performed. Morphologic examination disclosed 2 histologically distinct tumors, including a GH adenoma and a prolactin adenoma. Immunohistochemistry revealed Ki-67 labeling indices of 1% and 2%, respectively. Of significant note was MGMT immunopositivity in the GH adenoma and lack of staining in the prolactin adenoma. CONCLUSION: This is the first clinical instance in which MGMT was assessed in double adenomas of the pituitary. The 2 tumors showed significant differences in reactivity that could impact chemotherapeutic management. The adenomas underwent recurrence, a feature that reflects their invasive nature and the possibility that chemotherapeutic intervention may be required in the future. Response to temozolomide use is anticipated with respect to the prolactin adenoma but would likely not benefit the GH cell adenoma of our patient.

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 score0.461
Threshold uncertainty score0.783

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 integrity0.0000.001
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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations14
Published2010
Admission routes2
Has abstractyes

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