Inadequate diagnosis and management of temporomandibular disorders in the pediatric population of Canada
Bibliographic record
Abstract
Background: This review focuses on the wicked problem of inadequate management and diagnosis of temporomandibular disorders (TMD) in the Canadian pediatric populations. The lack of standardized TMD diagnostic criteria, relevant research, and insufficient clinician and patient education exacerbates this challenge. This literature review presents the relevant information available for TMD diagnostic criteria and management, clinician and patient TMD education, and sociodemographic factors that affect TMD in children and adolescents. Methods: Articles published between January 1st, 2001, and November 6th, 2023, in PubMed and Cochrane were analyzed and a total of 39 papers were selected and included in this literature review. Results:Several studies suggest the need for validated and reliable diagnostic criteria for TMD in children and adolescents since current screening and diagnosis methods are unreliable, ungeneralizable, and contradictory. Reported prevalence of pediatric TMD in literature varied from 5.1% to 68%, along with contradiction regarding the impact of sex differences. Additionally, the global literature highlights disparities in TMD education for both clinicians and patients, which results in ineffective TMD management. Necessity of early age diagnosis and management are also evident in literature analyzing the impact of sociodemographic and psychological factors on TMD. Conclusion: Tackling the interdisciplinary problem of TMD in pediatric populations through development of a validated and standardized global diagnostic criteria, improving professional and patient education, and conducting thorough research are crucial steps toward ensuring the effective management and diagnosis of TMD, ultimately promoting the overall well-being of pediatric populations.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".