Assessment of Pain Scales Used in Endodontic Postoperative Pain Evaluation: Frequency, Advantages, and Limitations
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.025 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".