ArticleVeterinary pathology2026
Development and implementation of a data parsing protocol for companion animal cancer data.
Article in Veterinary pathology, 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.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Companion animal cancer diagnostic reports are text-based documents containing essential information on tumor classification and diagnosis. Establishing an animal cancer registry requires integrating and extracting structured data from diverse report formats across multiple providers. This study presents the development of an object-oriented programming approach to standardize and automate cancer data collection for canine and feline patients, enabling the creation of the Australian Companion Animal Registry of Cancers (ACARCinom); Australia's first national registry of cat and dog cancers. An object-oriented programming approach was developed using the C# language for data processing, tested on sample data from 6 data providers. The initial programming phase focused on designing a parser that identified report sections using regular expressions based on standardized headings. The text was then cleaned to remove unnecessary formatting and HTML tags. Data dictionaries containing preferred terms and synonyms were used to extract key information such as diagnosis, topography, grade, and metastasis, improving consistency and accuracy. A coordinate map of extracted terms was generated to analyze spatial relationships within the report, allowing prioritization of diagnoses. The system also logged parsing decisions and potential issues for expert review. Markup using HTML tags enabled clear visualization of parsed content within the original reports. Extracted data and patient metadata were stored in an intermediary database table, allowing veterinary pathology experts to review and refine entries before final import. This automated solution streamlines data extraction and standardization from diverse sources, enabling the efficient analysis of cancer records and enhancing research and surveillance capacity in veterinary oncology.
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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.