Question · asked by 1 review

Supervised dosing with a long‐acting medication vs unsupervised consumption: Retention [

Supervised dosing with a long‐acting medication showed no clear difference in retention ‐ retention at the end of treatment (number of participants) compared with unsupervised consumption (low certainty).

What each review found

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FindingEffectStudiesGRADE certainty
Supervised dosing with a long‐acting medication vs unsupervised consumption
Retention ‐ retention at the end of treatment (number of participants) · patients with opioid dependence
Supervised dosing with a long‐acting medication showed no clear difference in retention ‐ retention at the end of treatment (number of participants) compared with unsupervised consumption (low certainty).
Supervised dosing with a long‐acting opioid medication in the management of opioid dependence (2017) · Cochrane Headline outcomes only
RR 0.99 [0.88, 1.12]4 studies · 716 participantsLow
Supervised dosing with a long‐acting medication vs unsupervised consumption
Retention ‐ retention at 3 months (number of participants) · patients with opioid dependence
Supervised dosing with a long‐acting medication showed no clear difference in retention ‐ retention at 3 months (number of participants) compared with unsupervised consumption (low certainty).
Supervised dosing with a long‐acting opioid medication in the management of opioid dependence (2017) · Cochrane Headline outcomes only
RR 0.94 [0.84, 1.05]3 studies · 472 participantsLow

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Supervised dosing with a long‐acting opioid medication in the management of opioid dependence
2017 · Cochrane Headline outcomes only
Not reportedlist not extracted yetNot assessed

Studies behind the findings

None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.

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.