Evaluating Dental Pain in Diverse Conditions: Findings From Short-Form McGill Pain Questionnaire Study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Making precise diagnoses of patients with dental pain is challenging. Thus, a screening system that can help accurately stratify patients depending on the severity of their condition is required. In this study, we compared the characteristics of dental pain in patients with different conditions using a Short-Form McGill Pain Questionnaire (SF-MPQ). METHODS: A total of 1541 patients admitted to the Peking University School of Stomatology between February 2022 and July 2023 were assessed. Demographic data and pain characteristics were collected. Pain was assessed using the SF-MPQ, which includes the Visual Analogue Scale (VAS), Present Pain Intensity (PPI), and Pain Rating Index (PRI). Statistical analysis was performed using SPSS software, with variance analysis and Pearson's correlation coefficients, and a T-test was employed to assess the relationship between pain scores and various factors. RESULTS: A total of 11 conditions associated with dental pain were detected. Symptomatic irreversible pulpitis (SIP, 427, 27.7%) and symptomatic apical periodontitis (SAP, 429, 27.8%) were the most common conditions. Higher scores were seen in patients with SIP (5.5 ± 2.8 for PRI; 59.4 ± 24.4 for VAS; 2.9 ± 0.8 for PPI), SAP (5.6 ± 2.9 for PRI; 56.1 ± 32.0 for VAS; 2.8 ± 0.7 for PPI) and interappointment flare-up (IFU, 8.2 ± 0.5 for PRI; 56.2 ± 14.9 for VAS; 3.0 ± 0.0 for PPI). The location of pain in a tooth, spontaneous pain, sleep disturbance, duration of pain in a short time, taking painkillers was invalid, diagnosis with SIP and SAP, and IFU were significantly associated with higher pain scores (all P < .05). CONCLUSIONS: Patients with SIP and SAP present with higher pain scores. The SF-MPQ can aid in the effectiveness of dental emergency triage by identifying true acute symptomatic patients and ensuring appropriate management based on the severity of their conditions. CLINICAL RELEVANCE: SF-MPQ may help to increase effectiveness in dental emergency triage, diagnosis, and treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".