ArticleFrontiers in physiology2026
An allometric large-scale analysis of the observed performance limits and sex differences in elite powerlifting.
Article in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Percentile-Based Normative Standards for Strength Assessment Beyond Linear Bodyweight Multipliers in Competitive Powerlifters.Sports medicine - open · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The objective normalization of athletic performance across diverse body masses remains one of the most persistent methodological challenges in strength and conditioning research. Historically, this normalization has been governed by the theoretical square-cube law (scaling exponent b = 0.67) or, more recently in strength sports, by highly parameterized polynomial algorithms such as the Wilks and International Powerlifting Federation General Load (IPF GL) formulas. This study challenges both geometric scaling and polynomial overfitting by deriving empirical allometric exponents from a tiered stratification design. Methods: A large-scale, highly curated dataset of powerlifters competing under performance-enhancing drug testing protocols was analyzed (N = 457,471). A stratified log-log regression model was implemented, progressively restricted to the absolute Top 20 all-time athletes per weight category (N = 1,833) to identify shifting scaling laws. Comparative statistical equivalence and agreement with the IPF GL formula were evaluated using Williams' t-test and Bland-Altman analysis. Results: The research identified a systemic phenomenon termed "allometric decay". As athletes approach absolute physiological limits, the scaling exponent meaningfully deviates from 0.67, decaying from general population baselines (males: b = 0.55; females: b = 0.50) down to 0.47 for elite males and 0.41 for elite females, at the Global All-Time Top 20 level. Despite its mathematical simplicity, the empirical allometric model achieved statistical equivalence with the complex IPF GL formula in mass-independence for elite males (Williams' t = -0.72, p = 0.47, Cohen's q = 0.001) and demonstrated extremely high-ranking agreement (mean bias = 0.00; 95% limits of agreement = ± 0.20 Z-scores). Conclusion: This significant decay indicates that the observed cost of acquiring functional muscle mass exhibits progressive diminishing returns at the elite level. Consequently, current polynomial scoring systems appear to be mathematically overfitting data to compensate for these underlying physiological ceilings. This study proposes a return to sex-specific empirical allometry, advocating for the adoption of these derived exponents as a more parsimonious, transparent, and biologically grounded method for cross-category strength comparison.
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.