Methodology for Knowledge Synthesis of the Management of Vaccination Pain and Needle Fear
Bibliographic record
Abstract
BACKGROUND: A knowledge synthesis was undertaken to inform the development of a revised and expanded clinical practice guideline about managing vaccination pain in children to include the management of pain across the lifespan and the management of fear in individuals with high levels of needle fear. This manuscript describes the methodological details of the knowledge synthesis and presents the list of included clinical questions, critical and important outcomes, search strategy, and search strategy results. METHODS: The Grading of Assessments, Recommendations, Development and Evaluation (GRADE) and Cochrane methodologies provided the general framework. The project team voted on clinical questions for inclusion and critically important and important outcomes. A broad search strategy was used to identify relevant randomized-controlled trials and quasi-randomized-controlled trials. Quality of research evidence was assessed using the Cochrane risk of bias tool and quality across studies was assessed using GRADE. Multiple measures of the same construct within studies (eg, observer-rated and parent-rated infant distress) were combined before pooling. The standardized mean difference and 95% confidence intervals (CI) or relative risk and 95% CI was used to express the effects of an intervention. RESULTS: Altogether, 55 clinical questions were selected for inclusion in the knowledge synthesis; 49 pertained to pain management during vaccine injections and 6 pertained to fear management in individuals with high levels of needle fear. Pain, fear, and distress were typically prioritized as critically important outcomes across clinical questions. The search strategy identified 136 relevant studies. CONCLUSIONS: This manuscript describes the methodological details of a knowledge synthesis about pain management during vaccination and fear management in individuals with high levels of needle fear. Subsequent manuscripts in this series will present the results for the included questions.
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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 | 0.316 | 0.591 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.043 | 0.031 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.076 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".