Investigation of factors that influence pain experienced and the use of pain medication following periodontal surgery
Bibliographic record
Abstract
AIMS: To determine the relationship between anticipated pain and actual pain experienced following soft tissue grafting or implant surgery; to identify the factors that predict actual pain experienced and the use of pain medication following soft tissue grafting or implant surgery. MATERIALS AND METHODS: Prior to dental implant placement (n = 98) or soft tissue grafting (n = 115) and for seven days following the procedure, patients completed a visual analog scale indicating anticipated or experienced pain, respectively. The use of pain medication and alcohol, and smoking were measured. RESULTS: Actual pain experienced on day 1 was lower (p < .01) than anticipated pain and continued to decrease (p ≤ .01) for each of the 7 consecutive days. Anticipated and actual pain were positively correlated. Increasing age (p < .05), having sedation during the surgery (p < .05), and lower use of pain pills (p < .01) predicted lower pain experienced. Actual pain experienced was a predictor of pain pill use (p < .01). Greater nervousness (p < .01) prior to surgery was a predictor of greater anticipated pain. CONCLUSIONS: Patients anticipated more pain than they actually experienced. Sedation, age and number of pain pills used predicted pain experienced. This trial was registered with clinicaltrials.gov as NCT03064178.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".