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Record W2160062436 · doi:10.1177/1352458512458555

Prolactin in multiple sclerosis

2012· review· en· W2160062436 on OpenAlexaff
Simon Zhornitsky, V. Wee Yong, Samuel Weiss, Luanne M. Metz

Bibliographic record

VenueMultiple Sclerosis Journal · 2012
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsMultiple sclerosisProlactinMedicinePathophysiologyOptic neuritisInternal medicineMyelinClinically isolated syndromeOncologyCentral nervous systemImmunologyHormone

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is more common among women than men. MS often goes into remission during pregnancy, when prolactin (PRL) levels are known to be high. In an animal model of demyelination, PRL promoted myelin repair, suggesting it has potential as a remyelinating therapy in MS. In this systematic review, we examined the known associations between PRL and MS, in order to elucidate its potential role in the pathophysiology and treatment of MS. A systematic search was performed in the electronic databases PubMed and EMBASE, using the keywords "prolactin" AND "multiple sclerosis." The inclusion criteria were met by 23 studies. These studies suggested to us that elevated PRL may be more common in MS patients than in controls. Hyperprolactinemia may also be associated with clinical relapse in MS, especially among patients with hypothalamic lesions or optic neuritis; however, it is unknown if this is a cause or consequence of a relapse. Overall, most people with MS have normal PRL levels. The impact of PRL on MS outcomes remains unclear.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.407
GPT teacher head0.376
Teacher spread0.032 · 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

Citations46
Published2012
Admission routes1
Has abstractyes

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