Temporomandibular Disorders and Hormones in Women
Bibliographic record
Abstract
Temporomandibular disorders (TMD) are loosely defined as an assorted set of clinical conditions, characterized by pain and dysfunction of the masticatory system. Pain in the masticatory muscles, in the temporomandibular joint (TMJ), and in associated hard and soft tissues, limitation in jaw function, and sounds in the TMJ are common symptoms. That women make up the majority of patients treated for TMD is extensively hypothesized and documented in numerous epidemiological studies. Certain contradictory studies exist which propose that there are no statistically significant gender differences in the actual incidence of changes in joint morphology. Nonetheless, extensive literature suggests the disorder is 1.5-2 times more prevalent in women than in men, and that 80% of patients treated for TMD are women. The severity of symptoms is also related to the age of the patients. Pain onset tends to occur after puberty, and peaks in the reproductive years, with the highest prevalence occurring in women aged 20-40, and the lowest among children, adolescents, and the elderly. The gender and age distribution of TMD suggests a possible link between its pathogenesis and the female hormonal axis. In this review, we will use the hypothesis that the overwhelming majority of patients treated for temporomandibular disorders are women and use the available literature to examine the role of hormones in TMD.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".