Clinical Pain Perception and Hormone Replacement Therapy in Postmenopausal Women Experiencing Orofacial Pain
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the magnitude of the relation between a postmenopausal woman's hormonal replacement status and clinical pain report in a sample of women experiencing orofacial pain. DESIGN: To accomplish this, pain ratings were collected during a routine chronic pain evaluation at an orofacial pain clinic from a sample of 87 postmenopausal women. RESULTS: Results of ANCOVA (controlling for pain duration) demonstrated that postmenopausal women receiving hormone replacement therapy (HRT) reported higher levels of pain than postmenopausal women not taking HRT. Numeric pain rating scales revealed large effect sizes for worst pain report (0.62), moderate differences for average (0.48) and current (0.39) pain levels, and trivial differences for least pain (0.04). Effect sizes for the McGill Pain Questionnaire indicated somewhat smaller differences (0.35-0.24) between the two groups. CONCLUSIONS: This study is among the first to examine the relation between a woman's hormonal status and clinical pain perception and is the first to investigate the role of HRT in a postmenopausal woman's orofacial pain report in a clinical treatment setting. This area of inquiry is particularly salient given the high percentage of women who choose to initiate HRT either after hysterectomy or with the onset of menopause.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".