A Sex-Specific Evaluation of Dental Students’ Ability to Perform Subgingival Debridement: Randomized Trial
Bibliographic record
Abstract
BACKGROUND: A successful periodontitis treatment demands good manual skills. A correlation between biological sex and dental students' manual dexterity is currently unknown. OBJECTIVE: This study examines performance differences between male and female students within subgingival debridement. METHODS: A total of 75 third-year dental students were divided by biological sex (male/female) and randomly assigned to one of two work methods (manual curettes n=38; power-driven instruments n=37). Students were trained on periodontitis models for 25 minutes daily over 10 days using the assigned manual or power-driven instrument. Practical training included subgingival debridement of all tooth types on phantom heads. Practical exams were performed after the training session (T1) and after 6 months (T2), and comprised subgingival debridement of four teeth within 20 minutes. The percentage of debrided root surface was assessed and statistically analyzed using a linear mixed-effects regression model (P<.05). RESULTS: The analysis is based on 68 students (both groups n=34). The percentage of cleaned surfaces was not significantly different (P=.40) between male (mean 81.6%, SD 18.2%) and female (mean 76.3%, SD 21.1%) students, irrespective of the instrument used. The use of power-driven instruments (mean 81.3%, SD 20.5%) led to significantly better results than the use of manual curettes (mean 75.4%, SD 19.4%; P=.02), and the overall performance decreased over time (T1: mean 84.5%, SD 17.5%; T2: mean 72.3%, SD 20.8%; P<.001). CONCLUSIONS: Female and male students performed equally well in subgingival debridement. Therefore, sex-differentiated teaching methods are not necessary.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".