Minimally Painful Local Anesthetic Injection for Cleft Lip/Nasal Repair in Grown Patients
Bibliographic record
Abstract
INTRODUCTION: There has been a recent interest in injecting large body and face areas with local anesthetic in a minimally painful manner. The method includes adherence to minimal pain injection details as well feedback from the patient who counts the number of times he feels pain during the injection process. This article describes the successes and limitations of this technique as applied to primary cleft lip/nasal repair in grown patients. METHODS: Thirty-two primary cleft lip patients were injected with local anesthesia by 3 surgeons and then underwent surgical correction of their deformity. At the beginning of the injection of the local anesthetic, patients were instructed to clearly inform the injector each and every time they felt pain during the entire injection process. RESULTS: The average patient felt pain only 1.6 times during the injection process. This included the first sting of the first 27-gauge needle poke. The only pain that 51% of the patients felt was that first poke of the first needle; 24% of the patients only felt pain twice during the whole injection process. The worst pain score occurred in a patient who felt pain 6 times during the injection process. Ninety-one percent of the patients felt no pain at all after the injection of the local anesthetic and did not require a top-up. CONCLUSION: It is possible to successfully and reliably inject local anesthesia in a minimally painful manner for cleft lip and nasal repair in the fully grown cleft patient.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".