Total intravenous sedation with target-controlled infusion in dentistry: clinical experience with the third-generation Eleveld pharmacokinetic/pharmacodynamic model
Bibliographic record
Abstract
Background: Approximately 31% of adults experience dental anxiety, which represents a significant barrier to timely care and ultimately results in poor oral health outcomes. Moderate sedation is an effective method of managing anxiety while preserving patient responsiveness. Total intravenous anesthesia (TIVA) combined with target-controlled infusion (TCI) systems, which utilize pharmacokinetic models, enable precise control of drug delivery by maintaining stable effect-site concentrations. This study evaluated the safety and efficacy of moderate TCI-administered sedation using an Eleveld pharmacodynamic-pharmacokinetic model for propofol and remifentanil in dental practice. Methods: This prospective study included 114 patients who underwent dental procedures at two clinical sites. Moderate sedation was delivered via a TCI-guided Eleveld model, targeting the effect-site concentrations of propofol and remifentanil. The primary outcome was the incidence of adverse events as defined by the Tracking and Reporting Outcomes of Procedural Sedation criteria. The secondary outcomes included procedural duration, drug dosing, vital sign fluctuations, and patient-reported satisfaction. Results: <90%), bradycardia (HR < 40 bpm), or clinical over-sedation. Hypotension (MAP < 65 mmHg) occurred in 13 patients who were managed conservatively without intervention. The average recovery time was 10.1 ± 5.2 min. Patient satisfaction was high (99%), with 93.6% of patients reporting little to no recall of the procedure. Conclusions: TCI-based moderate sedation using the Eleveld model for propofol and remifentanil is safe, effective, and well-tolerated in dental settings. The model offers predictable sedation depth, rapid recovery, and high levels of patient satisfaction, supporting its broader implementation in the management of dental anxiety.
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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".