The impact of cannabis use on local anesthetic dosing during hair restoration surgery: a case report, proposed mechanisms, and clinical recommendations
Bibliographic record
Abstract
Cannabis use has increased significantly in the last decade. This article presents a case where a patient needed more local anesthetic (LA) than usual to induce effective anesthesia during hair transplant surgery. The reason cannabis users often need more LA is poorly understood. One possibility is that cannabis withdrawal effect makes patients more sensitive to pain and stress. Additionally, vasodilatory property of cannabis may speed up LA clearance from the application site. The interactions of two major cannabinoids, cannabidiol (CBD) and tetrahydrocannabinol (THC), with cannabinoid receptor type 1 (CB1), cannabinoid receptor type 2 (CB2), and transient receptor potential vanilloid 1 (TRPV1) receptors are also complex. Furthermore, CBD and THC function as cytochrome P450 enzyme inhibitors potentially impacting systemic metabolism. When planning to administer LA during hair restoration surgery in cannabis users, clinicians should obtain a detailed history of prior consumption (type of cannabis, frequency, dosage). Preoperative planning should consider the anticipated duration of surgery and calculate the maximum safe LA dose to avoid the risk of toxicity. Also, patients should be carefully monitored for vital signs during surgery. If a patient requires frequent re-injection to remain pain free, the surgeon may need to re-assess the surgical plan to avoid toxicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".