A Systematic Review and Meta-analysis of Methods for Reducing Local Anesthetic Injection Pain Among Patients Undergoing Periocular Surgery
Bibliographic record
Abstract
PURPOSE: Various factors help minimize pain during the injection of local anesthetic. The majority of current evidence involves nonspecific injection sites. The objective of this review was to provide a comprehensive summary of all existing evidence for methods used to reduce injection pain specifically in the context of periocular procedures. METHODS: A literature search of the MEDLINE, EMBASE, and Scopus databases was conducted to identify all relevant experimental and observational studies from 1946 to 2018. Studies were included of patients undergoing periocular surgery under subcutaneous local anesthesia whereby outcomes were reported following a specific intervention intended to help reduce pain. Risk of bias was assessed using recognized tools. A subgroup meta-analysis was performed to indirectly compare pooled intervention-versus-control differences for various pain reduction interventions. RESULTS: Following the review of 2089 search results, 23 articles representing 1135 patients were included. The methods assessed in the studies included choice of anesthetic agent, buffering, warming, dilution, needle type, administration of an inhalational anesthetic, application of topical anesthetics, iontophoresis, skin cooling with ice, tactile distraction with vibration, and decreasing the rate of injection. CONCLUSIONS: Methods demonstrating best efficacy included solution modification (buffering, dilution, warming), skin cooling with ice, vibration, transconjunctival topical anesthetic before injection, and decreased rate of injection. Further study is warranted for modification of equipment factors, topical anesthetics, and strategies to reduce pain because of anesthetic infiltration.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".