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Record W4390766647 · doi:10.1007/s12020-023-03678-z

Are prolactin levels efficient in predicting a pituitary lesion in patients with hyperprolactinemia?

2024· article· en· W4390766647 on OpenAlexaff
Emanuele Varaldo, Daniela Cuboni, Nunzia Prencipe, Luigi Simone Aversa, Michela Sibilla, Fabio Bioletto, Alessandro Maria Berton, Valentina Gasco, Ezio Ghigo, Silvia Grottoli

Bibliographic record

VenueEndocrine · 2024
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
FundersUniversità degli Studi di Torino
KeywordsMedicineProlactinInternal medicineMagnetic resonance imagingCohortPituitary diseaseAbnormalityGastroenterologyEndocrinologyCirrhosisRetrospective cohort studyHormoneRadiology

Abstract

fetched live from OpenAlex

PURPOSE: Data regarding the presence of a prolactin (PRL) threshold above which a pituitary magnetic resonance imaging (MRI) is mandatory in patients with hyperprolactinemia (hyperPRL) are controversial and derived primarily from studies focused on female populations. Aim of our study was to evaluate in a cohort of patients of both sexes with confirmed hyperPRL, the possible correlation between PRL values and the presence of pituitary abnormalities. METHODS: We retrospectively analyzed data from patients who underwent serial PRL sampling at our Division between January 2015 and December 2022. Patients diagnosed with monomeric hyperPRL at serial sampling and with subsequent contrast-enhanced MRI results available for the pituitary region were included in the study. Exclusion criteria were prior pituitary disease, severe renal insufficiency, liver cirrhosis, uncompensated primary hypothyroidism and ongoing therapy with hyperprolactinemic drugs. Physiological causes of hyperPRL were also ruled out. RESULTS: Out of the 1253 patients who underwent serial PRL sampling, 139 patients (101 women and 38 men) met the inclusion criteria: 106 (76.3%) patients had some form of pituitary disease, with microlesions observed in 69.8%, macrolesions in 25.5% and other findings in 4.7% of subjects. PRL values showed a modest accuracy in predicting the presence of a pituitary abnormality and the best cut-offs identified were >25 µg/L (AUC 0.767, p = 0.003) and >44.2 µg/L (AUC 0.697, p < 0.001) in men and women, respectively; however, if only patients with PRL values > 500 µg/L were excluded from the analysis, as they were already supposed to harbor a macroprolactinoma, PRL levels were not able to predict the presence of a macrolesion neither in men nor women. CONCLUSION: Given the high prevalence of pituitary abnormalities in patients of both sexes with hyperPRL at serial sampling, performing a pituitary imaging in all cases of hyperPRL, even if mild, appears to be a cautious choice.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.263
Teacher spread0.248 · 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 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

Citations10
Published2024
Admission routes1
Has abstractyes

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