ArticleComputers in biology and medicine2025
When less is statistically Equivalent: Lobe-Specific optimization of histopathological assessment in SARS-CoV-2 pneumonia.
Article in Computers in biology and medicine, 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
5 authors.
Funding
Abstract
backgroundPrecise histopathological assessment of pulmonary lesions in animal models is fundamental to evaluating COVID-19 interventions. The multifocal, heterogeneous distribution of SARS-CoV-2-induced pathology in rhesus macaques presents a critical challenge: balancing comprehensive evaluation against resource efficiency. No statistically-validated sampling optimization exists for this widely-used model. We hypothesized that lobe-specific, statistically-validated sampling thresholds could maintain assessment accuracy while significantly reducing analytical burden.
methodsWe developed a semi-quantitative scoring system targeting interstitial pneumonia-the predominant histopathological feature in SARS-CoV-2-infected rhesus macaques (n = 12). Two ACVP board-certified pathologists independently evaluated 710-1634 high-power fields (40 × magnification) per animal across seven lung lobes, achieving substantial inter-rater reliability (Cohen's κ = 0.74). To determine minimum sampling requirements maintaining statistical equivalence with comprehensive assessment, we employed bootstrapping simulation (10,000 iterations) combined with Two One-Sided Tests (TOST) equivalence analysis (bounds: ±0.25 pathology points).
resultsOptimal sampling percentages exhibited significant lobe-specific variability: left caudal (25 %, p = 0.047), right caudal (30 %, p = 0.038), left/right proximal, (50 %, p = 0.044/p = 0.043), right accessory (50 %, p = 0.172), right middle (60 %, p = 0.049), and left middle (75 %, p = 0.084). Power analysis demonstrated robust detection capability (range: 0.45-0.72) at α = 0.05. These optimized parameters reduce required field assessments by 25-75 % while maintaining statistical equivalence.
conclusionThis first anatomically-stratified, statistically-validated methodology significantly enhances histopathological assessment efficiency in the rhesus macaque COVID-19 model. By establishing lobe-specific minimum sampling thresholds that preserve statistical equivalence, our approach optimizes resource utilization while maintaining sensitivity to detect intervention effects, potentially accelerates preclinical therapeutic evaluations.
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.