A Comparison of Three Pain Assessment Scales in the Assessment of Pain Among Dental Patients in Port Harcourt
Bibliographic record
Abstract
Proper assessment of pain is essential in evaluating the appropriate treatment need of patients presenting with dental conditions. This study aimed to determine the correlation between Short Form McGill pain questionnaire 2 (SF-MPQ-2), Visual analogue Scale (VAS) and Numerical Rating Scale (NRS) for pain assessment among dental patients. A total of 83 patients that presented at the Oral Diagnosis clinic of the University of Port Harcourt Teaching Hospital with various dental conditions over 2 months were recruited for the study. The severity of the different presenting conditions was determined using SF-MPQ-2, VAS and NRS. The mean pain assessment scores for the different dental conditions was compared and Pearson correlation coefficient was evaluated for the three pain assessment scales. P < 0.05 was considered statistically significant. The mean age of the study participants was 38.2 ± 14.0 years with age range of 16 to 83 years. The mean VAS and NRS scores were significantly higher in those diagnosed with acute apical periodontitis with mean scores of 6.68±2.36 and 6.61±2.06 respectively. The participants with cancer had the lowest SF-MPQ-2 mean scores while those with chronic periodontitis have the lowest score using VAS and NRS. There was a significant strong, positive correlation between VAS and NRS pain assessment tools. The correlation between SF-MPQ-2 and either VAS/NRS was however, weak but positive and statistically significant. Severity of pain was highest among those with acute apical periodontitis using the three pain assessment tools. There was a significant positive correlation between SF-MPQ-2, VAS and NRS for dental pain assessment.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".