Systematic Review of Brain Tumor Treatment in Dogs
Bibliographic record
Abstract
Intracranial neoplasia is commonly diagnosed in dogs and can be treated by a variety of methods, but formal comparisons of treatment efficacy are currently unavailable. This review was undertaken to summarize the current state of knowledge regarding outcome after the treatment of intracranial masses in dogs, with the aim of defining optimal recommendations for owners. This review summarizes data from 794 cases in 22 previously published reports and follows PRISMA guidelines for systematic review. A Pubmed search was used to identify suitable articles. These then were analyzed for quality and interstudy variability of inclusion and exclusion criteria and the outcome data extracted for summary in graphs and tables. There was a high degree of heterogeneity among studies with respect to inclusion and exclusion criteria, definition of survival periods, and cases lost to follow-up making comparisons among modalities troublesome. There is a need for standardized design and reporting of outcomes of treatment for brain tumors in dogs. The available data do not support lomustine as an effective treatment, but also do not show a clear difference in outcome between radiotherapy and surgery for those cases in which the choice is available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".