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Record W2066695412 · doi:10.3109/01674820903276438

Ovulation disturbances and mood across the menstrual cycles of healthy women

2009· article· en· W2066695412 on OpenAlexaff
Anne T. Harvey, Christine L. Hitchcock, Jerilynn C. Prior

Bibliographic record

VenueJournal of Psychosomatic Obstetrics & Gynecology · 2009
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOvulationLuteal phaseMenstrual cycleMoodAnovulationBasal body temperaturePsychologyFeelingFollicular phaseMenstruationEndocrinologyInternal medicinePhysiologyMedicineHormonePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

We examined the cyclicity of negative mood relative to ovulation and ovulation disturbances in Menstrual Cycle Diary(c) data collected daily during a 1-year study of ovulation, exercise, and bone change. A validated quantitative basal temperature-based methodology was used to determine the onset of the luteal phase. 'Feeling depressed', 'feeling anxious', and 'feeling angry/frustrated' were scored on a scale of 0 (absent) to 4 (very intense). Mood scores were examined over two 15-day intervals centered on either ovulation/midpoint, or on the onset of flow. Data were available from 765 cycles of 62 healthy and initially ovulatory women with a mean age of 33.9 +/- 5.4 years. Of 739 cycles that could be classified, 532 (72%) were normally ovulatory, 185 (25%) were ovulatory with a short (<10 day) luteal phase, and 22 (3%) were anovulatory. Minor cyclic mood changes were present in both ovulatory and anovulatory menstrual cycles. In anovulatory cycles, mood tended to be more variable but less negative, with a time course that differed from that in ovulatory cycles. Mood scores did not differ based on luteal phase length or with hormone levels. Patterns and mechanisms of mood change in very symptomatic women appear to be essentially amplifications of normal experiences.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.391
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.342
Teacher spread0.325 · 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

Citations24
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Psychosomatic Obstetrics & GynecologySame topicMenstrual Health and DisordersFrench-language works237,207