Evaluating economic outcomes in the management of temporomandibular disorders: a protocol for a systematic review of randomised controlled trials
Bibliographic record
Abstract
INTRODUCTION: Systematic reviews (SRs) on the management of temporomandibular disorders (TMDs) have predominantly focused on evaluating the effectiveness of various treatments, identifying those that provide the greatest benefits. However, the economic evaluation of these treatments has not been systematically explored. This SR aims to address this gap by evaluating the economic outcomes of the most common treatment modalities for TMDs, including cost-effectiveness, cost-utility, cost-benefit, cost-minimisation and the burden of illness. METHODS AND ANALYSIS: This SR will be conducted using the following electronic databases Business Source Complete, CINAHL, EconLit (ProQuest), Embase (Ovid), MEDLINE (PubMed), MEDLINE (Ovid) and Scopus to identify studies evaluating the economic outcomes of treatments for TMDs. The eligibility criteria are as follows: (1) studies examining the costs and/or impact of treatments for TMDs and (2) articles published between 2000 and 2025. The primary outcomes of interest are the economic findings outlined earlier. Data extraction will include the following: author(s), year of publication, country, study objectives, study design, eligibility criteria, TMD diagnosis and screening, study groups, randomisation, blinding, sample size, number of participants invited, enrolled and completed, duration of treatment, follow-up, study duration, settings, assessment instruments, study outcomes, statistical analyses, results, limitations, strengths and funding sources. The quality of studies will be evaluated using the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist, with risk of bias assessed using the Cochrane Effective Practice and Organization of Care's risk-of-bias tool; where applicable, the Outcome Reporting Bias in Trials will be used to detect reporting biases. A narrative synthesis and summary tables will outline study characteristics, economic outcomes and the overall quality of evidence. We will conduct qualitative secondary and sensitivity analyses. ETHICS AND DISSEMINATION: This SR does not require an ethics approval. The results will be disseminated through international and national conferences and peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42024613553.
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 | 0.180 | 0.213 |
| Meta-epidemiology (narrow) | 0.009 | 0.008 |
| Meta-epidemiology (broad) | 0.024 | 0.024 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.068 | 0.013 |
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".