Algorithm for Temporomandibular Disorders With Osteopathic Manipulative Therapy: An Expert Consensus
Bibliographic record
Abstract
Objective: The purpose of this study was to create a consensus algorithm for the osteopathic approach to temporomandibular dysfunctions Methods: A conventional Delphi method of at least 3 rounds of questionnaires was carried out by a panel of experts to reach a consensus on a flowchart algorithm for the diagnosis and treatment of temporomandibular disorders with osteopathic manipulative therapy Results: During the 3 rounds, a total of 7 panellists participated. Consensus was reached on 182 items (70.54% = 182/258). The algorithm has a numerical code with which the direction of each option can be followed, and a color code that shows the classification of each of the items (anamnesis, examination, observation, referral, palpation and treatment). The algorithm begins with 4 main categories: psychological aspects, exclusion criteria, physical aspects, and other issues, which are further subdivided into specific items that will be addressed according to the findings obtained during the patient visit. Conclusion: This study created an algorithm for the osteopathic approach to temporomandibular dysfunctions.
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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 | 0.078 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".