Analysis of Referral Pathways, Diagnosis, and Treatment Patterns in a University -Based Orofacial Multidisciplinary Pain Clinic
Bibliographic record
Abstract
Background- The prevalence of multifactorial chronic diseases is increasing globally. The objective of this study was to examine associations between chronic orofacial pain complaints and psychological distress in patients assessed at a multidisciplinary clinic inspired by the biopsychosocial model. Methods- A retrospective study design was employed to analyse data collected from patient charts recorded at the University of Alberta Multidisciplinary Orofacial Pain Clinic between 2018-2023. The team comprises a pharmacist, dietitian, family physician, oral and maxillofacial surgeon, psychologist, orofacial pain and oral medicine specialist, along with the oral medicine residents. Demographic, clinical variables, psychological were retrieved. The psychological variables included the Adverse Childhood Experiences (ACE) scale, Pain Catastrophizing Scale (PCS), and Injustice Experience Questionnaire (IEQ). To evaluate the associations between the severity of TMJ pain and headaches and psychological variable scores, Pearson’s chi-square test, Fisher’s exact test, and binomial logistic regression were performed. Results- The study analysed 288 charts of patients ranging in age from 13 to 93 years (mean age 46.69, SD 16.5). Most patients were female (82.6%) and resided primarily in Alberta (94.4%), with some also from Saskatchewan and British Columbia. Self-reported behaviors included tobacco smoking (15.5%), alcohol consumption (59.4%), and recreational drug use (15.5% current, 8.5% past). This study confirmed significant associations in patients with a moderate or severe risk of PCS. Among these, patients had 3.7 and 3.9 times higher odds of experiencing moderate to severe TMJ pain and headaches, respectively, compared to those with a low PCS risk. Additionally, patients with a high risk of IEQ had 2.8 times higher odds of experiencing moderate to severe headaches compared to those with a low IEQ risk. About 14.8% of patients did not answer the ACE, PCS, or IEQ variables and were thus excluded from analysis. Conclusion- Pain severity in chronic orofacial symptoms (TMJ pain and headaches) was associated with higher PCS scores. Similarly, higher IEQ scores correlated with increased headache severity. The significant number of patients who declined to answer the psychological assessments suggests underlying psychological factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".