Reovirus Salvage of Positive Resection Margin: A Novel Treatment Adjunct
Bibliographic record
Abstract
HYPOTHESIS: We hypothesized that the use of reovirus as an intraoperative adjunct would improve local control of positive margins in a human squamous cell carcinoma nude mouse model. PURPOSE: This study was designed to (1) develop a nude mouse human squamous cell carcinoma positive margin model and (2) assess the effect of adjunct intraoperative treatment with reovirus irrigation and injection on local control of resections with positive margins. MATERIALS AND METHODS: We developed a positive margin nude mouse model using the University of Michigan SCC-22B cell line. Established tumours in 39 mice were resected, leaving behind a 1 mm positive margin. The mice were then divided into five treatment groups: (1) no treatment was provided, (2) ultraviolet (UV)-inactivated reovirus was irrigated into the wound bed, (3) UV-inactivated reovirus was injected into the positive margin intratumorally and peritumorally, (4) reovirus was injected into the positive margin intratumorally and peritumorally, and (5) reovirus was irrigated into the wound bed. The mice were followed for 28 weeks and sacrificed. RESULTS: The results of the irrigations showed that tumours recurred in all (100%) of the control groups (no treatment and UV-inactivated virus). The mice irrigated with active reovirus solution had recurrence in 3 of the 14 sites (21%). These findings were statistically significant, with p > .001. The results of the injection showed that tumours recurred in all (100%) of the control groups (no treatment and UV-inactivated virus). The mice injected with reovirus solution had recurrence in 6 of the 16 sites (38%). These findings were statistically significant, with p > .007. CONCLUSIONS: Reovirus adjunctive treatment is a novel, safe, and effective method of improving local control in the positive margin human squamous cell carcinoma mouse model.
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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 | 0.000 |
| 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 | 0.001 |
| 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".