MétaCan
Menu
← Back to cohort
Record W7038862807

Investigating the Relationships between Menstrual Cycle, Progesterone, and Concussion in Female Athletes

2022· dissertation· W7038862807 on OpenAlexfundno aff

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersBranch Out Neurological Foundation
KeywordsConcussionMenstrual cycleAthletesNeuroprotectionHormoneAffect (linguistics)Traumatic brain injuryPoison control
DOInot available

Abstract

fetched live from OpenAlex

Sport related concussion is a traumatic brain injury induced by biomechanical forces. Progesterone, a female sex hormone, is thought to be neuroprotective and may have implications in concussion. The current study examined how changes in hormone levels may affect menstrual functioning and outcomes after concussion in university athletes. The influence of progesterone was examined in relation to variation in the menstrual cycle and the severity of symptoms post-concussion. Specifically, our findings suggest that there may be changes in menstrual cycle patterns and neuroendocrine disturbances occurring after concussion in female athletes. Additionally, progesterone may have a neuroprotective effect as its increased concentration is associated with decreases in symptom severity acutely after concussion. Findings from this study are informative for future research aiming to explore the influence of female sex hormones in the field of concussion and brain injury.

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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.128
GPT teacher head0.410
Teacher spread0.282 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTSpace→Same topicTraumatic Brain Injury Research→French-language works237,207→