Patient-perceived features and clinical characteristics of tooth pain : A comparison between apical periodontitis and persistent dento-alveolar pain disorder (PDAP) – preliminary results
Bibliographic record
Abstract
Background: \nDifferential diagnosis between tooth pain of inflammatory and neuropathic origin is crucial since treatment strategies differ. \nAim: Evaluate and compare self-reported and clinically observed pain characteristics in patients with SAP (symptomatic apical periodontitis) and PDAP (persistent dento-alveolar pain disorder). \nMethodology: Patients diagnosed with SAP and PDAP were recruited from Malmö University and Folktandvården Östergötland. Data collection included clinical examination and questionnaire (tooth pain characteristics, verbal pain description [short-form McGill Pain Questionnaire; SF-MPQ], factors affecting the pain). \nResults: Data from 24 patients with SAP and 20 with PDAP (24 females, mean age 53 years) were analyzed. Average pain intensity was 4.4 (0–10 numeric rating scale) and average duration 1527 days. 64% reported continuous and 27% recurrent pain. 58% of teeth were tender to percussion and 66% to apical palpation. 27% reported concurrent pain from jaw muscles/joints. Significant differences were found for gender (% females; PDAP>SAP;p=0.013), pain duration (PDAP>SAP;p<0.0001), pain frequency (PDAP>SAP;p<0.001), percussion tenderness (SAP>PDAP;p=0.012), muscle/joint pain (PDAP>SAP;p=0.021). SF-MPQ and affecting factors did not differ (p=0.096–1.000). \nConclusion: Preliminary results indicate that pain intensity, pain description and factors affecting the pain are similar for SAP and PDAP. Female gender, long pain duration, high pain frequency, and concurrent muscle/joint pain presented more frequently in PDAP.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".