Question · asked by 1 review

Clear aligners (Invisalign / thermoformed aligners) vs Conventional orthodontic appliance treatment: White spot lesion incidence

Population
Orthodontic patients undergoing comprehensive treatment (late mixed or permanent dentition, no active caries)
Intervention
Clear aligners (Invisalign / thermoformed aligners)
Comparison
Conventional orthodontic appliance treatment
Outcome
White spot lesion incidence

Most studies reported a lower incidence of white spot lesions with clear aligners than fixed appliances, but heterogeneous assessment methods precluded meta-analysis (ungraded).

What each review found

Current versions only
FindingEffectStudiesGRADE certainty
Clear aligners (CA) vs Conventional fixed orthodontic appliances (CF)
Incidence of white spot lesions (WSLs) · Orthodontic patients undergoing comprehensive treatment (late mixed or permanent dentition, no active caries) · End of orthodontic treatment (varying), University/hospital orthodontic clinics and private practice (multiple countries)
Most studies reported a lower incidence of white spot lesions with clear aligners than fixed appliances, but heterogeneous assessment methods precluded meta-analysis (ungraded).
White spot lesions, plaque accumulation and salivary caries-associated bacteria in clear aligners compared… (2023) All outcomes
Not meta-analyzed (heterogeneous methods; tooth-based vs patient-based incidence). 4 studies assessed WSL incidence; 3 (Albhaisi 2020 [12]; Buschang 2019 [29]; Dallel 2020 [30]) reported a lower risk of developing WSLs…4 studiesGRADE not reported

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
White spot lesions, plaque accumulation and salivary caries-associated bacteria in clear aligners compared to fixed orthodontic treatment.…
2023 All outcomes
May 2023 (searched January 2022 to May 2023; literature 1990-May 2023)14Critically Low / Low
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.