Towards a new taxonomy of idiopathic orofacial pain
Bibliographic record
Abstract
There is no current consensus on the taxonomy of the different forms of idiopathic orofacial pain (stomatodynia, atypical odontalgia, atypical facial pain, facial arthromyalgia), which are sometimes considered as separate entities and sometimes grouped together. In the present prospective multicentric study, we used a systematic approach to help to place these different painful syndromes in the general classification of chronic facial pain. This multicenter study was carried out on 245 consecutive patients presenting with chronic facial pain (>4 months duration). Each patient was seen by two experts who proposed a diagnosis, administered a 111-item questionnaire and filled out a standardized 68-item examination form. Statistical processing included univariate analysis and several forms of multidimensional analysis. Migraines (n=37), tension-type headache (n=26), post-traumatic neuralgia (n=20) and trigeminal neuralgia (n=13) tended to cluster independently. When signs and symptoms describing topographic features were not included in the list of variables, the idiopathic orofacial pain patients tended to cluster in a single group. Inside this large cluster, only stomatodynia (n=42) emerged as a distinct homogenous subgroup. In contrast, facial arthromyalgia (n=46) and an entity formed with atypical facial pain (n=25) and atypical odontalgia (n=13) could only be individualised by variables reflecting topographical characteristics. These data provide grounds for an evidence-based classification of idiopathic facial pain entities and indicate that the current sub-classification of these syndromes relies primarily on the topography of the symptoms.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".