ArticlePloS one2025
The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary outcomes.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
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Who cites it
4 citing papers in PubMed.
- RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI.Patterns (New York, N.Y.) · 2026Article
- Better Accuracy for Better Science . . . Through Random Conclusions.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
- The Mode Effect of Web-Based Surveying on the 2018 U.S. Health and Retirement Study Measure of Cognitive Functioning.The journals of gerontology. Series B, Psychological sciences and social sciences · 2023Article
- Linguistic, cultural, and narrative capital: computational and human readings of transfer admissions essays.Journal of computational social science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Understanding the "fit" of models designed to predict binary outcomes has been a long-standing problem across the social sciences. We propose a flexible, portable, and intuitive metric for quantifying the change in accuracy between two predictive systems in the case of a binary outcome: the InterModel Vigorish (IMV). The IMV is based on an analogy to weighted coins, well-characterized physical systems with tractable probabilities. The IMV is always a statement about the change in fit relative to some baseline model-which can be as simple as the prevalence-whereas other metrics are stand-alone measures that need to be further manipulated to yield indices related to differences in fit across models. Moreover, the IMV is consistently interpretable independent of baseline prevalence. We contrast this metric with alternatives in numerous simulations. The IMV is more sensitive to estimation error than many alternatives and also shows distinctive sensitivity to prevalence. We consider its performance using examples spanning the social and natural sciences. The IMV allows for precise answers to questions about changes in model fit in a variety of settings in a manner that will be useful for furthering research and the understanding of social outcomes.
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What OpenQuestion holds
Registered trials
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.