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Macroprolactinemia in a Patient with Infertility and Hyperprolactinemia

2006· article· en· W2404241885 on OpenAlexaff
Hasnain Khandwala

Bibliographic record

VenueSouthern Medical Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineProlactinInfertilityDopamine agonistDopaminePediatricsInternal medicinePregnancyHormoneDopaminergic

Abstract

fetched live from OpenAlex

A significant number of patients with hyperprolactinemia have macroprolactinemia, a condition characterized by the preponderance of big-big prolactin with normal levels of free prolactin. As macroprolactin does not have biologic activity, such patients do not require further investigations or treatment for hyperprolactinemia. The case of a patient with hyperprolactinemia diagnosed during investigation of secondary infertility is presented. She was treated for over 2 years with dopamine agonists, with which her prolactin level normalized, but she remained infertile. Subsequent investigations demonstrated that she suffered from macroprolactinemia, not true hyperprolactinemia. The patient is currently not on dopamine agonist therapy, and although her total prolactin levels remain significantly elevated, her free prolactin levels have been in the normal range. Physicians should familiarize themselves with this entity and consider testing for it in patients with hyperprolactinemia to avoid an inappropriate diagnosis and unnecessary treatment.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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 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

Citations5
Published2006
Admission routes1
Has abstractyes

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