ArticleRoyal Society open science2025
A systematic approach to classifying and evaluating heterogeneity measures.
Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces a systematic framework for analysing heterogeneity through three principal measure classes: dispersion-based, expected-difference and divergent approaches. I demonstrate that these classes capture distinct structural aspects, with graph heterogeneity measures incorporating global topology beyond degree counts while degree-focused approaches quantify connectivity variation. Key findings establish that apparent inconsistencies across measures reflect heterogeneity's complex nature rather than methodological flaws. The framework enables context-appropriate measure selection for applications ranging from epidemiological modelling to cyber security, while highlighting the critical distinction between degree-focused and topology-aware heterogeneity quantification. The work advances network science by mapping methodological trade-offs and proposing future development of tunable hybrid measures for complex systems analysis.
Indexed as
Identifiers
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.