Predictors of pain associated with routine procedures performed in general dental practice
Bibliographic record
Abstract
OBJECTIVES: The objective of the study was to investigate factors that influence pain intensities associated with routine dental procedures. METHOD: Four hundred and fifty-one dental patients self-reported pain experienced during the procedure immediately after undergoing a variety of common dental interventions and 1 day after the completion of the procedure. Pain character was measured using the McGill short-form pain questionnaire and intensity using a numerical rating (NRS) scale. Information was collected on a number of factors that could influence pain: dental anxiety was measured using the Corah Dental Anxiety Scale to categorize patients into four domains (fearless, some unease, nervous and very anxious). Dentists provided information regarding the type(s) of procedure and use of local anaesthetic (LA). RESULTS: Seventy-five percent of patients (339/451) reported no pain during their procedure when the data were collected immediately postoperatively (NRS score = 0). Univariate analyses showed that dental anxiety, LA use and type of procedure (extractions) were significant (P < 0.05) predictors of reported intra-operative pain. However, when these factors were combined in a multivariate model, the strongest predictor of pain was dental anxiety [odds ratio (OR) = 4.98 (95% CI 1.42-17.44)] and LA use [OR = 2.79 (95% CI 1.39-5.61)]. Although the strongest predictor of postoperative pain on the next day was pain reported during the procedure [OR = 5.85 (95% CI 2.71-12.64)], LA remained a significant predictor of pain the day after the procedure [OR = 3.16 (95% CI 1.02-9.81)]. CONCLUSIONS: Dentists need to assess their patients both preoperatively for dental anxiety and intra-operatively for signs of suboptimal local anaesthesia so as to effectively align patient management and clinical techniques to control dental anxiety and produce adequate anaesthesia.
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".