Poor neck posture and longer working duration during root canal treatment correlated with increased neck discomfort in dentists with <5-years' experience in endodontics
Bibliographic record
Abstract
OBJECTIVE: This study investigated the effects of neck posture and working duration during each step of root canal treatment (i.e. opening the canal [OC], length determination, mechanical instrumentation, try main cone, and filling the root canal) on neck discomfort (ND) in dentists with <5-years' endodontic experience. METHODS: Twenty-four dentists performed a one-visit endodontic treatment of an upper molar in a phantom head model. A video was recorded to evaluate the dentists᾽ neck postures using the Modified-Dental Operator Posture Assessment Instrument (M-DOPAI) and treatment duration. The M-DOPAI divides the dentists᾽ neck postures into three categories: acceptable, compromised, or harmful posture. The participants rated their ND using Borg᾽s CR-10 scale every 10 min. and at the end of each treatment step. The relationships between neck posture/treatment duration and Borg᾽s CR-10 scores were examined using partial correlation. RESULTS: The number of compromised and harmful neck postures during the endodontic procedure (r = 0.43, P = .04) and treatment duration (r = 0.58 P = .005) significantly correlated with ND at the end of treatment. The number of compromised and harmful neck postures during the OC step (r = 0.75, P < .001) and the duration of the OC step (r = .70, P < .001) significantly correlated with ND at the end of the step. CONCLUSION: Poor neck postures and long working duration during endodontic treatment correlated with ND among inexperienced dentists. Neck pain interventions should focus on neck postures and work duration during root canal treatment, particularly in the OC step.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".