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Record W2123543192 · doi:10.1016/j.pain.2014.08.027

Ovarian hormones and chronic pain: A comprehensive review

2014· review· en· W2123543192 on OpenAlexaff
Samah Hassan, Abi Muere, Gillian Einstein

Bibliographic record

VenuePain · 2014
Typereview
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsHormoneMigraineMenstrual cycleEndometriosisPelvic painMedicinePhysiologyChronic painInternal medicineBioinformaticsPhysical therapySurgeryBiology

Abstract

fetched live from OpenAlex

Most chronic noncancer pain (CNCP) conditions are more common in women and have been reported to worsen, particularly during the peak reproductive years. This phenomenon suggests that ovarian hormones might play a role in modulating CNCP pain. To this end, we reviewed human literature aiming to assess the potential role of ovarian hormones in modulating the following CNCP conditions: musculoskeletal pain, migraine headache, temporal mandibular disorder, and pelvic pain. We found 50 relevant clinical studies, the majority of which demonstrated a correlation between hormone changes or treatments and pain intensity, threshold, or symptoms. Taken together, the findings suggest that changes in hormonal levels may well play a role in modulating the severity of CNCP conditions. However, the lack of consistency in study design, methodology, and interpretation of menstrual cycle phases impedes comparison between the studies. Thus, while the literature is highly suggestive of the role of ovarian hormones in modulating CNCP conditions, serious confounds impede a definitive understanding for most conditions except menstrual migraine and endometriosis. It may be that these inconsistencies and the resulting lack of clarity have contributed to the failure of hormonal effects being translated into medical practice for treatment of CNCP conditions.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.067
GPT teacher head0.383
Teacher spread0.316 · 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.

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

Citations113
Published2014
Admission routes1
Has abstractyes

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