A retrospective comparison of first and second opinion histopathology with patient outcomes in veterinary oncology cases (2011–2019)
Bibliographic record
Abstract
Mandatory second opinion histopathology is common practice in human surgical pathology. It is intended to confirm the original diagnosis or identify clinically significant discrepancies, which could alter the course of disease, cost of treatment, patient management or prognosis. This retrospective analysis aimed to evaluate agreement between first and second opinion histopathology cases, examine their correlation with natural history of disease and investigate the rationale for pursuing this test. Medical records from 2011 to 2019 were reviewed, identifying 109 cases where second opinion histopathology was sought. Reasons for seeking second opinion and clinical disease course were also reviewed to determine whether case progression favoured first or second opinion findings in cases of diagnostic disagreement. Diagnostic disagreement was found in 49.5% of cases. Complete diagnostic disagreement (a change in degree of malignancy or tumour type) occurred in 15.6% cases and partial disagreement (a change in tumour subtype, grade, margins and mitotic count) occurred in 33.9%. Major disagreement (a change in diagnosis resulting in alteration of treatment recommendations) occurred in 38.5% of cases. The most common reasons for seeking second opinion were an atypical/poorly differentiated tumour (31.2%; 34/109) or a discordant clinical picture (24.8%; 27/109). Among cases with any form of disagreement, natural history of disease favoured second opinion findings in 33.3%. The first opinion was favoured over the second in a single case. These findings reinforce previous literature supporting a role for second opinion histopathology in optimizing therapy and predicting outcomes in veterinary oncology, particularly in cases where diagnosis is in question based on the overall clinical picture.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".