Evidence map›Paper›PMID 40691748›Full record

ReviewQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2025

Quality of patient-reported outcome measures and treatment effects: a review of orthopaedic randomized trials.

Joel J Gagnier, Edith Otalike, Andrew D Firth

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Review in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Joel J GagnierDepartment of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada. Jgagnie4@uwo.ca.ORCID http://orcid.org/0000-0002-3162-3935
Edith OtalikeDepartment of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
Andrew D FirthDepartment of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study assessed how poor-quality patients reported outcome measures (PROMS) influence treatment effect estimates in randomized patients with anterior cruciate ligament (ACL) injury.

methodsWe chose a representative sample of studies by searching top journals of orthopaedic surgery for randomized trials in patients after anterior cruciate ligament injury over the past 5-years. Studies were eligible if they reported at least one of the PROMs assessed in a previous review of their measurement properties. The risk of bias was assessed, and standardized treatment effects were calculated between groups for each PROM and time point. We used mixed-effects regression to predict treatment effect using psychometric summary score, risk of bias score, follow-up duration, sample size, proportion of female patients, and funding source as predictors, accounting for the random effect of the study.

resultsOut of the 4038 studies screened, 38 RCTs that used 9 validated PROMs and reported 139 PROM scores were included. The mean risk of bias score was 7.1 (standard deviation [SD]: 1), and the mean psychometric score was 1.4 (SD = 2.5). While psychometric quality was not significantly associated with treatment effect variance in lower-quality PROMs, the study found that longer follow-up duration was associated with a less likely treatment effect favouring the experimental group.

conclusionPoor quality patient-reported outcomes may demonstrate greater variance of treatment effects in randomized trials. More psychometric evidence, particularly of responsiveness, is needed to assess the effect of individual measurement properties on treatment effects. These findings should be considered hypothesis generating only and be followed up in additional related research.

Indexed as

Anterior Cruciate Ligament InjuriesPatient Reported Outcome MeasuresQuality of LifeRandomized Controlled Trials as TopicFemaleHumansMalePsychometricsTreatment OutcomeOrthopaedicsPatient-reported outcome measuresPsychometricsRisk of bias

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.