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Record W2533220077 · doi:10.1530/edm-16-0069

Late presentation of acromegaly in medically controlled prolactinoma patients

2016· article· en· W2533220077 on OpenAlexaff
Ekaterina Manuylova, Laura M. Calvi, Catherine Hastings, G. Edward Vates, Mahlon D. Johnson, William T. Cave, Ismat Shafiq

Bibliographic record

VenueEndocrinology Diabetes and Metabolism Case Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
Fundersnot available
KeywordsProlactinomaAcromegalyMedicinePituitary adenomaInternal medicineProlactinEndocrinologyAdenomaGrowth hormoneHormone

Abstract

fetched live from OpenAlex

Co-secretion of growth hormone (GH) and prolactin (PRL) from a single pituitary adenoma is common. In fact, up to 25% of patients with acromegaly may have PRL co-secretion. The prevalence of acromegaly among patients with a newly diagnosed prolactinoma is unknown. Given the possibility of mixed GH and PRL co-secretion, the current recommendation is to obtain an insulin-like growth factor-1 (IGF-1) in patients with prolactinoma at the initial diagnosis. Long-term follow-up of IGF-1 is not routinely done. Here, we report two cases of well-controlled prolactinoma on dopamine agonists with the development of acromegaly 10-20 years after the initial diagnoses. In both patients, a mixed PRL/GH-cosecreting adenoma was confirmed on the pathology examination after transsphenoidal surgery (TSS). Therefore, periodic routine measurements of IGF-1 should be considered regardless of the duration and biochemical control of prolactinoma. LEARNING POINTS: Acromegaly can develop in patients with well-controlled prolactinoma on dopamine agonists.The interval between prolactinoma and acromegaly diagnoses can be several decades.Periodic screening of patients with prolactinoma for growth hormone excess should be considered and can lead to an early diagnosis of acromegaly before the development of 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.248
Teacher spread0.240 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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