The Effect of Phentolamine on Reversing Soft Tissue Anesthesia
Bibliographic record
Abstract
Long-lasting numbness of soft tissues such as lips, tongue, and cheeks after dental anesthesia is not only unpleasant but has the potential to cause self-inflicted damage to the numb tissue. Phentolamine mesylate, when injected in vicinity to the site of local anesthesia, accelerates the absorption of local anesthesia. Dental use of this drug was approved in the United States and Canada in 2008 and 2014, respectively. The rapidly increasing popularity of this novel technology (intraoral phentolamine injections) warrants a health technology assessment for clinicians. A medical librarian conducted a systematic literature search (up to March 1, 2016) for any clinical study involving intraoral phentolamine injection. Meta-analysis of the efficacy data from 4 clinical studies supports the role of intraoral phentolamine injections in shortening the duration of numbness after local anesthesia. No publication bias was found in the selected studies. The selected studies identified no serious adverse events other than pain at the site of injection and some postprocedural pain. Our cost-effectiveness analysis shows phentolamine mesylate to be an effective treatment modality when compared with no treatment, sham, or placebo injection. Phentolamine mesylate incurs an additional cost (in US dollars) of $0.13 to $0.16 per minute of reversing the soft tissue local anesthesia and $0.38 to $0.46, when compared with sham or placebo injection, after a noninvasive dental procedure. The literature lacked substantial evidence in favor of clinical benefits, such as a decrease in self-inflicted injuries. Only a subgroup of the dental patient population undergoing specific dental procedures would benefit from accelerated recovery from numbness. Knowledge Transfer Statement: Intraoral phentolamine to reverse numbness is a new intervention with ambiguous utility. With consideration of cost and patient preference, evidence generated by this report may be used in clinical decision making and case selection for this intervention.
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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 | 0.006 | 0.001 |
| 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 | 0.001 |
| 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 | 0.001 |
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 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".