Routine Immunization Practices: Use of Topical Anesthetics and Oral Analgesics
Bibliographic record
Abstract
BACKGROUND: Immunization pain is a global public health issue. Despite an abundance of data that demonstrate the efficacy of local anesthetics for decreasing immunization pain, their adoption in practice has not been determined. Our objective was to evaluate analgesic use during childhood immunization. PATIENTS AND METHODS: We used a cluster-sampling survey of pediatricians in the greater Toronto area (who administer immunizations) and multiparous women. By using a self-administered survey, pediatricians reported frequency of analgesic use in their practice for 2 phases of immunization: injection (needle puncture and vaccine administration) and postinjection (hours to days postvaccination). By using an interviewer-administered face-to-face survey, mothers reported analgesic practices for their children. RESULTS: Of 195 eligible pediatricians, 140 (72%) responded. During the injection phase, 58% rarely or never used analgesics compared with 11% for the postinjection phase. During injection, the local anesthetics lidocaine-prilocaine and tetracaine were used at least sometimes in 12% and 2% of the practices, respectively, whereas acetaminophen and ibuprofen were used in 81% and 46%, respectively. Postinjection, acetaminophen and ibuprofen were used in 89% and 56% of practices. Of 257 eligible mothers, 200 (78%) participated. During injection, analgesics were used in 25% of immunizations (acetaminophen [87%], ibuprofen [7%], and lidocaine-prilocaine [6%]). Postinjection, analgesics were used in 33% of immunizations (acetaminophen [86%] and ibuprofen [14%]). CONCLUSIONS: A minority of pediatricians and mothers use topical local anesthetics during childhood immunization despite evidence to support their use. Oral analgesics are used more commonly, but this practice is not consistent with scientific evidence. Knowledge-translation strategies are needed to increase the use of local anesthesia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".