Question · asked by 1 review

Audiovisual distraction vs Conventional treatment: Self- or observational assessment of intraoperative distress/pain/acceptance of treatment during provision of LA: pain-related behaviour during LA (negative vs positive behaviour; Frankl Behaviour Rating Scale)

Population
Children and adolescents having dental treatment under local anaesthetic without general anaesthesia
Intervention
Audiovisual distraction
Comparison
Conventional treatment
Outcome
Self- or observational assessment of intraoperative distress/pain/acceptance of treatment during provision of LA: pain-related behaviour during LA (negative vs positive behaviour; Frankl Behaviour Rating Scale)

It is very uncertain whether audiovisual distraction changes self- or observational assessment of intraoperative… compared with conventional treatment (very low certainty).

What each review found

Include earlier review versions
FindingEffectStudiesGRADE certainty
Audiovisual distraction vs Conventional treatment
Self- or observational assessment of intraoperative distress/pain/acceptance of treatment during provision of LA: pain-related behaviour during LA (negative vs positive behaviour; Frankl Behaviour Rating Scale) · Children and adolescents having dental treatment under local anaesthetic without general anaesthesia · During LA
It is very uncertain whether audiovisual distraction changes self- or observational assessment of intraoperative… compared with conventional treatment (very low certainty).
Interventions for increasing acceptance of local anaesthetic in children and adolescents having dental… (2020) · Cochrane All outcomes
RR 0.13 [0.03, 0.50]1 study · 60 participantsVery low

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Interventions for increasing acceptance of local anaesthetic in children and adolescents having dental treatment
2020 · Cochrane All outcomes
2019-05Not listedNot assessed
How reviews are matched to this question

Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.