Relationship Between Estrogen and Idiopathic Mandibular Condylar Resorption: A Systematic Literature Review
Bibliographic record
Abstract
Background and Objectives: Pain in the TMJ is the second most common in the orofacial region. The objective of this systematic review was to assess whether a decrease in estrogen levels increases the risk of idiopathic condylar resorption by reviewing relevant literature and evidence. Material and Methods: This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A comprehensive search was performed in the PubMed (Medline), Science Direct (Elsevier), and Web of Science electronic databases. Results: The initial database search identified a total number of 453 studies. After applying the selection criteria, 36 articles were selected for a full-text analysis, and nine studies involving 1105 patients were included in the systematic review. According to the Newcastle–Ottawa Scale (NOS), two of the included articles were graded as being of “Moderate” quality and one was of “Fair” quality. After evaluating the rest of the articles according to the AXIS tool for cross-sectional studies, we generally found that the reliability is moderate. The results show that the decrease in estrogen promotes the occurrence of inflammation in the temporomandibular joint, and some sources mention that it increases the occurrence of idiopathic joint resorption, but we did not establish a complete correlation between the level of estrogen and idiopathic joint resorption. Conclusions: This systematic review indicates that there is no evidence suggesting that fluctuations in estrogen levels contribute to idiopathic mandibular condylar resorption, but reduced estrogen levels can be associated with chronic pain in the temporomandibular joint.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".