Assessment of Pain Scales Used in Endodontic Postoperative Pain Evaluation: Frequency, Advantages, and Limitations
Why this work is in the frame
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Bibliographic record
Abstract
Objectives: Postoperative pain is a critical outcome in endodontic research and clinical practice, directly impacting patient satisfaction and treatment success. Various pain assessment tools, such as the Visual Analog Scale (VAS) and Numeric Rating Scale (NRS), are employed to quantify and evaluate pain. This study aimed to analyze the frequency of use of different pain assessment tools in endodontic postoperative pain research across different databases. Materials and Methods: A bibliometric analysis was performed using PubMed, Web of Science, and Scopus. The search strategy included commonly used pain scales: VAS, NRS, Heft-Parker Visual Analog Scale, Verbal Rating Scale, Faces Pain Scale, Short-Form McGill Pain Questionnaire, and Brief Pain Inventory. The results were synthesized to determine the prevalence of these scales in published research. Results: VAS was the most frequently used tool, with 571 studies in PubMed (75.4%), 581 in Scopus (77.8%), and 346 in Web of Science (74.1%). The NRS followed, with 65 (8.6%), 71 (9.5%), and 51 (10.9%) studies, respectively. Other scales, such as the Heft-Parker Visual Analog Scale, Verbal Rating Scale, and Faces Pain Scale, were used less frequently. Comprehensive tools like the Short-Form McGill Pain Questionnaire and Brief Pain Inventory had minimal representation. Conclusion: VAS and NRS dominate endodontic postoperative pain research, reflecting their ease of use and widespread acceptance. Less commonly used tools, while valuable in specific contexts, are underrepresented. Future research should explore the reasons for this disparity and assess the potential of hybrid tools to standardize pain evaluation practices.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it