High Volume Local Anesthesia as a Postoperative Factor of Pain and Swelling in Dental Implants
Bibliographic record
Abstract
OBJECTIVE: To determine whether the administration of high-volume local anesthesia can influence postoperative pain and swelling, and the degree of patient satisfaction, following dental implant placement. MATERIAL AND METHODS: One hundred patients (45 women and 55 men) between 19 and 80 years old were divided into two groups: group A (n = 50, with placement of an implant using an atraumatic approach in each patient, with sub-periosteal injection of a volume of Ultracain(®) ≤0.9 mL [half a carpule]) and group B (n = 50, involving the same surgical procedure but infiltrating a local anesthetic volume of ≥7.2 mL [four carpules]). Visual analog scales were used in all patients to rate intraoperative pain and postoperative pain and swelling. After the first week, the patients completed a questionnaire evaluating satisfaction with treatment. RESULTS: The intraoperative pain scores were similar in both groups (p = 0.363), while the postoperative pain and swelling scores were significantly lower in group A at all time points. Patient rated satisfaction with the surgical treatment was higher in group A. CONCLUSIONS: Excess injected volume of local anesthetic in dental implant surgery has a negative impact upon both postoperative pain and swelling, and on patient rated satisfaction.
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 | 0.000 | 0.003 |
| 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) | 0.001 | 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 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".