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Record W4414195667 · doi:10.1007/s40265-025-02223-8

Update on Medical Treatment of Cushing’s Syndrome

2025· review· en· W4414195667 on OpenAlexaff
Brendan R. Dillon, Nidhi Agrawal, Yair Schwarz, Kristen Dancel-Manning, Antoine Tabarin, André Lacroix, Leo J. Hofland, Richard A. Feelders

Bibliographic record

VenueDrugs · 2025
Typereview
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsPharmacotherapySomatostatinDiseaseMedical treatmentAdverse effectMedical therapyEtiologyGlucocorticoid

Abstract

fetched live from OpenAlex

First-line treatment of endogenous Cushing's syndrome (CS) is surgical removal of the tumor responsible for cortisol excess. However, medical therapy has an established role in treatment when patients are not surgical candidates or decline surgery, residual or recurrent disease is present and not amenable to repeat resection, and control of hypercortisolism is needed either preoperatively or while awaiting the effects of radiotherapy. The approach to medical therapy should be tailored based on the etiology, degree of hypercortisolism, and patient characteristics. Currently available medical therapy for all etiologies of CS either blocks adrenal production of cortisol or blocks its action at the level of the glucocorticoid receptor. Currently available medical therapy for Cushing's disease (CD) targets the adrenocorticotropic hormone-secreting pituitary tumor through activation of somatostatin and dopamine receptors, alkylating DNA damage, or immune system activation. More focused therapy with greater efficacy and fewer adverse effects is needed, particularly in the case of CD, with potential targets and drugs identified and in development.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.003

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 head0.025
GPT teacher head0.346
Teacher spread0.321 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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