Pain measurement in oral and maxillofacial surgery
Bibliographic record
Abstract
Regardless of whether it is acute or chronic, the assessment of pain should be simple and practical. Since the intensity of pain is thought to be one of the primary factors that determine its effect on a human's overall function and sense, there are many scales to assess pain. The aim of the current article was to review pain intensity scales that are commonly used in dental and oral and maxillofacial surgery (OMFS). Previous studies demonstrated that multidimensional scales, such as the McGill Pain Questionnaire, Short form of the McGill Pain Questionnaire, and Wisconsin Brief Pain Questionnaire were suitable for assessing chronic pain, while unidimensional scales, like the Visual Analogue Scales (VAS), Verbal descriptor scale, Verbal rating scale, Numerical rating Scale, Faces Pain Scale, Wong-Baker Faces Pain Rating Scale (WBS), and Full Cup Test, were used to evaluate acute pain. The WBS is widely used to assess pain in children and elderly because other scales are often difficult to understand, which could consequently lead to an overestimation of the pain intensity. In dental or OMFS research, the use of the VAS is more common because it is more reliable, valid, sensitive, and appropriate. However, some researchers use NRS to evaluate OMFS pain in adults because this scale is easier to use than VAS and yields relatively similar pain scores. This review only assessed pain scales used for post-operative OMFS or dental pain.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".