Peritumoral Edema in Canine Extra‐Axial Brain Tumours: Effect of Steroids
Bibliographic record
Abstract
ABSTRACT This multicenter retrospective study evaluated the effects of a time delay and steroids on the volume of peritumoral edema (VPTE) in dogs with extra‐axial brain tumours. The hypothesis is that VPTE will decrease between the diagnostic (MRI‐1) and RT planning (MRI‐2) MRIs following the administration of steroids. Inclusion required paired MRI acquisitions within 3 months, with VPTE contouring for each MRI registered to the RT planning CT. No edema was defined as < 0.2 cm3, increased edema was > 30% VPTE increase and decreased edema was > 30% VPTE decrease. Forty‐four dogs of which 34 (77%) received steroids between MRIs were included. The median time between the MRIs was 22 days (range: 8–74 days). Nine (20%) had no edema on both MRIs. The median MRI‐1/VPTE: 0.83 cm3 (IQR: 0.15–2.06 cm3) and median MRI‐2/VPTE: 0.40 cm3 (IQR: 0.06–1.12 cm3) were significantly different (p = 0.048). Compared to MRI‐1/VPTE: 17 (39%) VPTE decreased, eight were stable and 10 increased. The median VPTE difference was −21%, range: −100 to +6287. With steroids, VPTE decreased in 15/34 (44%) and increasedin 6/34 (18%) (median VPTE diff: −60%) compared to no steroids (median VPTE diff: +25%). Steroids use was associated with change in VPTE (p = 0.009). Two dogs had clinical deterioration and were on steroids with documented VPTE increase (+86% and +1880%) without tumour progression. The change in VPTE is highly variable but reduction is associated with steroids. Notably, subjective improvement of clinical signs can be seen without significant decrease to the VPTE on imaging.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".