Introducing Evidence Based Orthodontics Journal Club Using A Structured Pre And Post Test
Bibliographic record
Abstract
Objective: To evaluate the impact of introducing evidence based orthodontics journal club on the performance of postgraduate residents using a structured pre-test and post-test. Study Design and Setting: The comparitive cross sectional study was conducted among the orthodontic postgraduate residents (n=30) of third year and final year at the orthodontic department at de’Montmorency College of Dentistry, Lahore. Methodology: Present study was conducted among the orthodontic postgraduate residents (n=30) of third year and final year at the orthodontic department at de’Montmorency College of Dentistry, Lahore. Questions were extracted from the journal club articles. These questions were structured and used in journal club as pre-test and post-test during the academic year 2015-16 and comparison of the performance in the pre-test and post-test over the course of the year was done. Results: The results of pre-test showed a statistically significant increase during the academic year (p=0.031). Performance in the post-test also showed a statistically significant increase during the academic year (p=0.001). Conclusion: The redesigning of structured pre and post test in orthodontic journal club resulted in significant improvement in the performance of postgraduate orthodontic residents
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".