Impact of risk of generalizability biases in adult obesity interventions: A meta‐epidemiological review and meta‐analysis is a research paper published in Obesity Reviews (2021). On theSindex it has a DataRank of 0.808. It has been cited 17 times, with 6 citing works in its 1-hop citation network. Its calibrated FAIR score is 68/100.
Biases introduced in early-stage studies can lead to inflated early discoveries. The risk of generalizability biases (RGBs) identifies key features of feasibility studies that, when present, lead to reduced impact in a larger trial. This meta-study examined the influence of RGBs in adult obesity interventions. Behavioral interventions with a published feasibility study and a larger scale trial of the same intervention (e.g., pairs) were identified. Each pair was coded for the presence of RGBs. Quantitative outcomes were extracted. Multilevel meta-regression models were used to examine the impact of RGBs on the difference in the effect size (ES, standardized mean difference) from pilot to larger scale trial. A total of 114 pairs, representing 230 studies, were identified. Overall, 75% of the pairs had at least one RGB present. The four most prevalent RGBs were duration (33%), delivery agent (30%), implementation support (23%), and target audience (22%) bias. The largest reductions in the ES were observed in pairs where an RGB was present in the pilot and removed in the larger scale trial (average reduction ES -0.41, range -1.06 to 0.01), compared with pairs without an RGB (average reduction ES -0.15, range -0.18 to -0.14). Eliminating RGBs during early-stage testing may result in improved evidence.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.434
From this paper's citation signal
Citation Network Contribution
0.374
From 6 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 54% comes from its base citations and 46% from the citation network (6 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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