Question · asked by 1 review

Bilateral extraction of compromised mandibular first permanent molars vs Age- and gender-matched non-extraction controls: Lower incisor inclination (lingual tipping and retrusion)

Population
Children (approximately 11 years or older) with compromised mandibular first permanent molars
Intervention
Bilateral extraction of compromised mandibular first permanent molars
Comparison
Age- and gender-matched non-extraction controls
Outcome
Lower incisor inclination (lingual tipping and retrusion)

Bilateral lower first permanent molar extraction was associated with lingual tipping and retrusion of the lower incisors in one case-control study; ungraded.

What each review found

Include earlier review versions
FindingEffectStudiesGRADE certainty
Bilateral extraction of compromised mandibular first permanent molars vs Age- and gender-matched non-extraction controls
Lower incisor inclination (lingual tipping and retrusion) · Children (approximately 11 years or older) with compromised mandibular first permanent molars · Private clinics (Brazil)
Bilateral lower first permanent molar extraction was associated with lingual tipping and retrusion of the lower incisors in one case-control study; ungraded.
Consequences of early extraction of compromised first permanent molar: a systematic review. (2018) All outcomes
Normando and Cavacami: lower incisor lingual tipping (1.NB mean 23.2 vs 28.4 degrees, P = 0.004) and retrusion (P = 0.03) after bilateral lower FPM loss. Table 3: Category III, Class C.1 studyGRADE not reported

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Consequences of early extraction of compromised first permanent molar: a systematic review.
2018 All outcomes
May 2015 to 2017 (literature from 1960 to 2017)19Critically Low / Critically Low

Studies behind the findings

The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.

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.