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Record W2735376278 · doi:10.1186/s11556-017-0179-1

Physical activity does not alter prolactin levels in post-menopausal women: results from a dose-response randomized controlled trial

2017· article· en· W2735376278 on OpenAlexafffund
Darren R. Brenner, Yibing Ruan, Andria R. Morielli, Kerry S. Courneya, Christine M. Friedenreich

Bibliographic record

VenueEuropean Review of Aging and Physical Activity · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsUniversity of CalgaryUniversity of AlbertaAlberta Health Services
FundersCanadian Cancer Society Research InstituteAlberta InnovatesAlberta Cancer FoundationEli Lilly and Company
KeywordsProlactinMedicineRandomized controlled trialInternal medicineConfidence intervalBreast cancerEndocrinologyPhysiologyCancerHormone

Abstract

fetched live from OpenAlex

BACKGROUND: Increased circulating levels of prolactin have been associated with increased risk of both in situ and invasive breast cancer. We investigated whether or not physical activity had a dose-response effect in lowering plasma levels of prolactin in postmenopausal women. METHODS: Four hundred previously inactive but healthy postmenopausal women aged 50-74 years of age were randomized to 150 or 300 min per week of aerobic physical activity in a year-long intervention. Prolactin was measured from fasting samples with a custom-plex multiplex assay. RESULTS: A high compared to moderate volume of physical activity did not reduce plasma prolactin levels in intention-to-treat (Treatment Effect Ratio (TER) 1.00, 95% Confidence Interval (CI) 0.95 - 1.06) or per-protocol analyses (TER 1.02, 95% CI 0.93 - 1.13). CONCLUSIONS: It is unlikely that changes in prolactin levels mediate the reduced risk of breast cancer development in post-menopausal women associated with increased levels of physical activity. TRIAL REGISTRATION: clinicaltrials.gov identifier: NCT01435005.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.030
GPT teacher head0.340
Teacher spread0.309 · 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 designRandomized trial
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
Published2017
Admission routes2
Has abstractyes

Explore more

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