Diagnostic criteria for temporomandibular disorders (DC/TMD) for children and adolescents: An international Delphi study—Part 1‐Development of Axis I
Bibliographic record
Abstract
BACKGROUND: Since in children and adolescence prevalence is assessed mainly on self-reported or proxy-reported signs and symptoms; there is a need to develop a more comprehensive standardised process for the collection of clinical information and the diagnosis of TMD in these populations. OBJECTIVE: To develop new instruments and to adapt the diagnostic criteria for temporomandibular disorders (DC/TMD) for the evaluation of TMD in children and adolescents. METHOD: A modified Delphi method was used to seek international consensus among TMD experts. Fourteen clinicians and researchers in the field of oro-facial pain and TMD worldwide were invited to participate in a workshop initiated by the International Network for Orofacial Pain and Related Disorders Methodology (INfORM scientific network) at the General Session of the International Association for Dental Research (IADR, London 2018), as the first step in the Delphi process. Participants discussed the protocols required to make physical diagnoses included in the Axis I of the DC/TMD. Thereafter, nine experts in the field were added, and the first Delphi round was created. This survey included 60 statements for Axis I, and the experts were asked to respond to each statement on a five-item Likert scale ranging from 'Strongly disagree' to 'Strongly agree'. Consensus level was set at 80% agreement for the first round, and at 70% for the next. RESULTS: After three rounds of the Delphi process, a consensus among TMD experts was achieved and two adapted DC/TMD protocols for Axis I physical diagnoses for children and adolescents were developed. CONCLUSION: Through international consensus among TMD experts, this study adapted the Axis I of the DC/TMD for use in evaluating TMD in children and adolescents.
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 | 0.058 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".