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Record W4398221087 · doi:10.1136/bcr-2023-258701

Immune checkpoint inhibitor-induced hypophysitis with transient ACTH-dependent hypercortisolism

2024· article· en· W4398221087 on OpenAlexaff
Fatima AlRubaish, Nisha Gupta, Meng Shi, Stavroula Christopoulos

Bibliographic record

VenueBMJ Case Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsHypophysitisMedicineAdrenal insufficiencyAdrenal crisisAdrenocorticotropic hormoneNivolumabIpilimumabHydrocortisoneInternal medicinePrimary Adrenal InsufficiencyHypothalamic diseaseEndocrinologyImmunotherapyPituitary glandHypogonadotropic hypogonadismHormoneCancer

Abstract

fetched live from OpenAlex

A woman in her 70s with metastatic melanoma presenting with refractory hypokalaemia on combined immune checkpoint inhibitors, nivolumab-ipilimumab, was diagnosed with adrenocorticotropic hormone (ACTH)-dependent hypercortisolism 11 weeks following the initiation of her immunotherapy. Investigations also demonstrated central hypothyroidism and hypogonadotropic hypogonadism. She underwent imaging studies of her abdomen and brain which revealed normal adrenal glands and pituitary, respectively. She was started on levothyroxine replacement and had close pituitary function monitoring. Two weeks later, her cortisol and ACTH levels started to trend down. She finally developed secondary adrenal insufficiency and was started on hydrocortisone replacement 4 weeks thereafter.This report highlights a case of immunotherapy-related hypophysitis with well-documented transient central hypercortisolism followed, within weeks, by profound secondary adrenal insufficiency. Healthcare professionals should remain vigilant in monitoring laboratory progression in these patients. Early recognition of the phase of hypercortisolism and its likely rapid transformation into secondary adrenal insufficiency can facilitate timely hormonal replacement and prevent complications.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.857

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 head0.020
GPT teacher head0.289
Teacher spread0.269 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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