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Record W4389001344 · doi:10.1136/bmjsem-2023-001814

Effect of menstrual cycle and contraceptive pill phase on aspects of exercise physiology and athletic performance in female athletes: protocol for the <i>Feminae</i> international multisite innovative project

2023· article· en· W4389001344 on OpenAlexaff
Kirsty J. Elliott‐Sale, T.R. Flood, Shawn M. Arent, Eimear Dolan, Bryan Saunders, Mette Hansen, Johanna K. Ihalainen, Ritva S. Taipale, Clare Minahan, Jane S Thornton, Kathryn E. Ackerman, Constance Lebrun, Craig Sale, Trent Stellingwerff, Paul Swinton, Anthony C. Hackney

Bibliographic record

VenueBMJ Open Sport & Exercise Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of AlbertaCanadian Sport Centre PacificWestern University
FundersAustralian Institute of SportNovo Nordisk FondenEnglish Institute of Sport
KeywordsAthletesMenstrual cyclePillMedicineProtocol (science)Physical therapyMenstruationPhysiologyAlternative medicineHormoneEndocrinologyInternal medicinePharmacology

Abstract

fetched live from OpenAlex

' has become the slogan for sport and exercise physiology-based research in female athletes. However, in most instances, it is challenging to address this gap of high-quality research in elite female athletes at a single study site due to challenges in recruiting enough participants with numerous menstrual cycle and contraceptive pill permutations. Accordingly, we have assembled an international multisite team to undertake an innovative project for female athletes, which investigates the effects of changes in endogenous and exogenous oestrogen and progesterone/progestins across the menstrual cycle and in response to second-generation combined monophasic contraceptive pill use, on aspects of exercise physiology and athletic performance. This project will employ the current gold-standard methodologies in this area, resulting in an adequately powered dataset. This protocol paper describes the consortium-based approach we will undertake during this study.

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)0.002
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 score0.400
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.046
GPT teacher head0.423
Teacher spread0.377 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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