Precision in Parsing: Evaluation of an Open‐Source Named Entity Recognizer (<scp>NER</scp>) in Veterinary Oncology
Bibliographic record
Abstract
Integrating Artificial Intelligence (AI) through Natural Language Processing (NLP) can improve veterinary medical oncology clinical record analytics. Named Entity Recognition (NER), a critical component of NLP, can facilitate efficient data extraction and automated labelling for research and clinical decision-making. This study assesses the efficacy of the Bio-Epidemiology-NER (BioEN), an open-source NER developed using human epidemiological and medical data, on veterinary medical oncology records. The NER's performance was compared with manual annotations by a veterinary medical oncologist and a veterinary intern. Evaluation metrics included Jaccard similarity, intra-rater reliability, ROUGE scores, and standard NER performance metrics (precision, recall, F1-score). Results indicate poor direct translatability to veterinary medical oncology record text and room for improvement in the NER's performance, with precision, recall, and F1-score suggesting a marginally better alignment with the oncologist than the intern. While challenges remain, these insights contribute to the ongoing development of AI tools tailored for veterinary healthcare and highlight the need for veterinary-specific models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".