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Record W4295860722 · doi:10.1111/jne.13197

The role of progesterone in memory bias during spatial navigation in females

2022· review· en· W4295860722 on OpenAlexafffund
Eamonn L. Gomez‐Perales, Wayne G. Brake

Bibliographic record

VenueJournal of Neuroendocrinology · 2022
Typereview
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAllopregnanoloneInternal medicineEndocrinologyStriatumPrefrontal cortexNeuroactive steroidHippocampusHormoneNeuroscienceEstrogenPsychologyMedicineDopamineReceptorCognition

Abstract

fetched live from OpenAlex

Rats can use several memory systems to navigate a maze toward a reward. Two of these are place memory and response memory and female rats can be biased to predominantly use one over another. Both progesterone and estrogens have been shown to alter memory bias. Although the effects of estrogens have been well documented, the effects of progesterone remain somewhat unexplored. Mechanisms through which progesterone may be acting to exert its effects are reviewed here. Converging evidence suggests that the actions of progesterone differ depending on the presence of estrogens, frequently acting in opposition to estrogens when administered together. The hippocampus, dorsal striatum, and prefrontal cortex are likely involved, as is the progesterone metabolite, allopregnanolone. There is a need for more research on progesterone and memory bias, especially considering current formulations of hormonal contraceptives include progestins.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.109
GPT teacher head0.345
Teacher spread0.236 · 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
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

Citations7
Published2022
Admission routes2
Has abstractyes

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