MétaCan
Menu
Back to cohort
Record W2003230512 · doi:10.1089/jwh.2006.15.57

Expert Guidelines for the Treatment of Severe PMS, PMDD, and Comorbidities: The Role of SSRIs

2006· review· en· W2003230512 on OpenAlexaff
Meir Steiner, Teri Pearlstein, Lee S. Cohen, Jean Endicott, Susan G. Kornstein, Carla P. Roberts, David L. Roberts, Kimberly A. Yonkers

Bibliographic record

VenueJournal of Women s Health · 2006
Typereview
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsPremenstrual dysphoric disorderMoodMenstruationPsychiatryMedicineAnxietyExacerbationLuteal phaseMenstrual cyclePsychologyInternal medicineHormone

Abstract

fetched live from OpenAlex

The hallmark feature of premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) is the predictable, cyclic nature of symptoms or distinct on/offness that begins in the late luteal phase of the menstrual cycle and remits shortly after the onset of menstruation. PMDD is distinguished from PMS by the severity of symptoms, predominance of mood symptoms, and role dysfunction, particularly in personal relationships and marital/family domains. Several treatment modalities are beneficial in PMDD and severe PMS, but the selective serotonin reuptake inhibitors (SSRIs) have emerged as first-line therapy. The SSRIs can be administered continuously throughout the entire month, intermittently from ovulation to the onset of menstruation, or semi-intermittently with dosage increases during the late luteal phase. These guidelines present practical treatment algorithms for the use of SSRIs in women with pure PMDD or severe PMS, PMDD and underlying subsyndromal clinical features of mood or anxiety, or premenstrual exacerbation of a mood/anxiety disorder.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0090.009

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 head0.156
GPT teacher head0.469
Teacher spread0.313 · 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 designNot applicable
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

Citations149
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Women s HealthSame topicMenstrual Health and DisordersFrench-language works237,207