A randomized controlled educational study to evaluate an e-learning module to teach the physical examination of the temporomandibular joint in juvenile idiopathic arthritis
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to evaluate the effectiveness of a novel e-learning module in teaching the physical exam of the temporomandibular joint (TMJ) in Juvenile idiopathic arthritis (JIA.). METHODS: An e-learning module was developed to convey the TMJ physical examination maneuvers that are considered to be best practice in JIA. Pediatric rheumatology fellows were randomized to two groups. One group received an article describing the physical examination skills while the second group received both the article and module. All participants completed a written pre-test, an in-person objective structured clinical examination (OSCE), a written post-test, and a follow-up survey. RESULTS: Twenty-two pediatric rheumatology fellows enrolled, with 11 per group. Written test: The two groups improved equally, although there was a trend toward improved defining of maximal incisal opening (MIO) in the module group. OSCE: The mean OSCE score was 11.1 (SD 3.3) in the article group and 13.5 (SD 1.9) in the module group (p = 0.06); significant differences were seen in measuring MIO (p = 0.01), calculating maximal unassisted mouth opening (MUMO; p = 0.01), and assessment of facial symmetry (p = 0.03), all favoring the module. Enjoyment scores in the module group were higher than in the article group (mean 7.7/10 vs. 5.9/10, p = 0.02). The two groups self-reported performing TMJ examinations at comparable rates three months following the intervention. CONCLUSIONS: The study demonstrated that a formalized educational program improved knowledge of the physical exam of the TMJ in JIA. Learners viewing the module were more adept at obtaining quantitative TMJ measurements.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".