Anesthesia for intra-articular corticosteroid injections in juvenile idiopathic arthritis: A survey of pediatric rheumatologists
Bibliographic record
Abstract
OBJECTIVE: To determine the methods of anesthesia currently being used by pediatric rheumatologists when performing intra-articular corticosteroid injections (IACI). STUDY DESIGN: A questionnaire was emailed to all members of the Childhood Arthritis & Rheumatology Research Alliance, a pediatric rheumatology research network in North America. The questionnaire consisted of 11 questions ranging from procedure technique, treatments prescribed for topical anesthesia and oral analgesia, and factors that might affect procedural pain. RESULTS: Seventy-four of 161 physicians (46%) responded to the questionnaire. On average, each physician injected 33 children (median 25, range 1-160) and 43 joints (median 30, range 1-150) yearly. Local anesthesia was used in children on average >/= 8 years (range 2-16 years), with general anesthesia being more frequently used for younger children. All respondents used local anesthesia. The most commonly used methods of local anesthesia were EMLA((R) )cream plus subcutaneous lidocaine (58.8%), ethyl chloride spray only (39.7%), EMLA((R) )cream only (33.8%), subcutaneous lidocaine only (25%), and lidocaine iontophoresis only (11.8%). Buffering of the lidocaine was routinely done only 7.4% of the time. CONCLUSION: Although pediatric rheumatologists in North America perform IACI on a large number of patients each year, a wide variety of methods are used to deliver local anesthesia with no accepted standard of care. More studies are needed to determine the optimal method of local anesthesia delivery to minimize pain associated with IACI.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".