The intriguing evolution of effect sizes in biomedical research over time: smaller but more often statistically significant
Paul Monsarrat; Jean‐Noël Vergnes · 2017 · GigaScience
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Background: In medicine, effect sizes (ESs) allow the effects of independent variables (including risk/protective factors or treatment interventions) on dependent variables (e.g., health outcomes) to be quantified. Given that many public health decisions and health care policies are based on ES estimates, it is important to assess how ESs are used in the biomedical literature and to investigate potential trends in their reporting over time. Results: Through a big data approach, the text mining process automatically extracted 814 120 ESs from 13 322 754 PubMed abstracts. Eligible ESs were risk
Abstract by Paul Monsarrat; Jean‐Noël Vergnes, GigaScience (2017) — licensed CC BY 4.0.
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WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1093/gigascience/gix121