Endodontic file separation and its management among dentists in Punjab, Pakistan
Bibliographic record
Abstract
The study aimed to find the incidence and awareness of endodontic instrument separation and its management among dental house officers, postgraduate trainees, demonstrators, consultants, and general dentists. Methods: This online questionnaire-based cross-sectional study was conducted with the approval of the IRB in private and public dental hospitals and dental clinics in Punjab. The authors developed the survey tool, which comprises 24 closed-ended items regarding demographics, the incidence of file separation, and awareness about its management. The data were analyzed using IBM SPSS version 24. The Chi-Square Test was used to compare percentages of categorical variables. Results: Postgraduate trainees experienced the most instrument separations (43.6%), made the most retrieval attempts (49.2%), and experienced the most secondary errors during retrieval (52.1%) (p<0.001). Around four out of ten respondents always informed the patients (39.6%) and department (41.6%) about errors. Manual files (69.8%), stainless steel files (75.8%), and short files (60.4%) were more frequently separated, and the most frequent cause was older fatigue files (57.7%). Manual files were more frequently broken in public dental institutes (p=0.003). Two-thirds of the file separations (72.5%) occurred during cleaning and shaping in the apical third of molars (65.1%), especially in mesiolingual canal (56.4%). Bypass attempt was the most common in symptomatic teeth (47.7%). Conclusions: Preventive approaches such as limiting file reuse and constructing a glide path can reduce the occurrence of file separation. Operators should be familiar with the number of uses of the instrument before fatigue and should be trained through workshops and refresher courses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".