Clinical-dosimetric relationship between lacrimal gland dose and keratoconjunctivitis sicca in dogs with sinonasal tumors treated with radiation therapy
Bibliographic record
Abstract
BACKGROUND: Dogs with sinonasal tumor can develop keratoconjunctivitis sicca (KCS) after radiation therapy (RT). In humans, the incidence of xerophtalmia is associated with the mean radiation dose received by the ipsilateral lacrimal gland (LG). HYPOTHESIS/OBJECTIVES: The eyes receiving a higher mean LG dose are more likely to develop KCS. The aim of the study was to determine a starting threshold dose to use as dose constraint for intensity-modulated radiation therapy (IMRT). ANIMALS: Dogs with nasal tumors treated with RT between August 2013 and December 2016. METHODS: Case control retrospective study of dogs with sinonasal tumor treated with 42 Gray (Gy) in 10 fractions using IMRT. Dogs were included if development of KCS after RT was documented (cases) or adequate follow-up information with Schirmer tear test (STT) result for ≥6 months after RT was available (controls). Lacrimal glands were contoured and dose distribution was calculated using the original treatment plan to determine prescribed doses to LGs. RESULTS: Twenty-five dogs were treated with RT and 5 dogs (20%) developed KCS. Fifteen dogs met the inclusion criteria including 5 unilateral KCS and 10 control dogs, resulting in 5 KCS eyes and 25 control eyes. KCS developed at a median of 111 days (84-122) after 1st RT. The mean LG dose reached using a 4.2 Gy per fraction was 33.08 Gy (range: 23.75-42.33) for KCS eyes and 10.33 Gy (1.8-24.77) for control eyes (P < .001). The minimum LG mean dose for developing KCS was 23.75 Gy. No eyes that received a mean LG dose <20 Gy developed KCS versus 5/7 (71%) developed with >20 Gy. CONCLUSION AND CLINICAL IMPORTANCE: Contouring and applying a dose constraint on LGs should be performed when using IMRT in dogs with sinonasal tumors to reduce the risk of KCS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".