The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Principles for Valid Histopathologic Scoring in Research
Open accessAbstract
Histopathologic scoring is a tool by which semiquantitative data can be obtained from tissues. Initially, a thorough understanding of the experimental design, study objectives, and methods is required for the pathologist to appropriately examine tissues and develop lesion scoring approaches. Many principles go into the development of a scoring system such as tissue examination, lesion identification, scoring definitions, and consistency in interpretation. Masking (aka “blinding”) of the pathologist to experimental groups is often necessary to constrain bias, and multiple mechanisms are available. Development of a tissue scoring system requires appreciation of the attributes and limitations of the data (eg, nominal, ordinal, interval, and ratio data) to be evaluated. Incidence, ordinal, and rank methods of tissue scoring are demonstrated along with key principles for statistical analyses and reporting. Validation of a scoring system occurs through 2 principal measures: (1) validation of repeatability and (2) validation of tissue pathobiology. Understanding key principles of tissue scoring can help in the development and/or optimization of scoring systems so as to consistently yield meaningful and valid scoring data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Schulz (1995) · doi:10.1001/jama.1995.03520290060030 · Cited by 5,634
On the Theory of Scales of Measurement
Stevens, S. S. (1946) · Science · doi:10.1126/science.103.2684.677 · Cited by 4,761 · cited 1×
“This method assigns data into defined categorical groups that are arranged in an ‘‘ordered’’ progression in lesion severity.(66) For example, a scoring system can be based on the estimated percentage of the tracheal wall that is affected by a lesion; in this case, a score (0–4) may be assigned (Table 5).”
MethodRaise standards for preclinical cancer research
Begley, C. G., & Ellis, L. M. (2012) · Nature · doi:10.1038/483531a · Cited by 2,979
Believe it or not: how much can we rely on published data on potential drug targets?
Prinz, F., Schlange, T., & Asadullah, K. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3439-c1 · Cited by 1,888
Guidelines for the Design and Statistical Analysis of Experiments Using Laboratory Animals
Festing, M. F. W., & Altman, D. G. (2002) · ILAR Journal · doi:10.1093/ilar.43.4.244 · Cited by 1,110
Identifying and Avoiding Bias in Research
Pannucci, C. J., & Wilkins, E. G. (2010) · Plastic and Reconstructive Surgery · doi:10.1097/prs.0b013e3181de24bc · Cited by 1,079
Cited by
Use of Severity Grades to Characterize Histopathologic Changes
Schafer, K. A., Eighmy, J., Fikes, J. D., Halpern, W. G., Hukkanen, R. R., Long, G. G., Meseck, E. K., Patrick, D. J., Thibodeau, M. S., Wood, C. E., et al. (2018) · Toxicologic Pathology · doi:10.1177/0192623318761348 · Cited by 230
Bone morphogenetic protein 8B promotes the progression of non-alcoholic steatohepatitis
Vacca, M., Leslie, J., Virtue, S., Lam, B. Y. H., Govaere, O., Tiniakos, D., Snow, S., Davies, S., Petkevicius, K., Tong, Z., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0214-9 · Cited by 60
Assessment of Skin Toxicity in an in Vitro Reconstituted Human Epidermis Model Using Deep Learning
Hu, F., Santagostino, S. F., Danilenko, D. M., Tseng, M., Brumm, J., Zehnder, P., & Wu, K. C. (2022) · The American Journal of Pathology · doi:10.1016/j.ajpath.2021.12.007 · Cited by 13
Alsaedi, A. Q., Nader, M. A., El-Kashef, D. H., & Abdelmageed, M. E. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1572758 · Cited by 5
Metadata
- Authors
- Gibson-Corley, K. N., Olivier, A. K., & Meyerholz, D. K.
- Venue
- Veterinary Pathology
- Publisher
- SAGE Publications
- Published
- 2013-04-04
- Volume
- 50
- Issue
- 6
- Pages
- 1007-1015
- ISSN
- 0300-9858 (ISSN-L)
- 1544-2217
- OpenAlex
- W2153866915
- Cited by
- 936
- References
- 7
- FWCI
- 24.77
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
