Heterogeneity of temporomandibular disorders: cluster and case–control analyses
Bibliographic record
Abstract
Cluster analysis has been applied to the classification of temporomandibular disorders (TMD). The factors most often used in these classification systems are psychological and psychosocial. The aim of this study is to classify individuals diagnosed with TMD, by using cluster analysis based on their clinical condition and the global severity of the disease. In this study, one investigator selected the patients at the dental clinics located at the Jewish General and Montreal General hospitals, Montreal, Canada, from September 1994 to December 1997. The study population included 162 outpatients. The results of this study indicated the existence of four TMD subgroups: three TMD pain groups with localized or generalized disorder related to different levels of interferences in their life and a non-pain, but disabled group. External validation of the cluster solution support the replication of the four groups and allow for further interpretation of the patients' profiles. Clenching-grinding and depression were related to the groups presenting generalized TMD. Orthodontic treatment and female sex, however, were the factors associated with a more localized condition. This classification system may provide a better understanding of the TMD subgroups and clues for the treatment and prognosis of TMD patients.
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 | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".