Intravital Microscopy (IVM) in Human Solid Tumors: Novel Protocol to Examine Tumor-Associated Vessels
Bibliographic record
Abstract
BACKGROUND: Intravital microscopy (IVM) allows the real-time, direct visualization of microscopic blood vessels. This pilot clinical trial will elucidate the physical and functional characteristics of vessels associated with solid tumors. OBJECTIVE: The main objective of this study is to determine the feasibility of performing IVM in patients with solid tumors during the standard course of surgical resection. IVM will also be performed when vasopressors or fluid boluses are administered during the standard course of the operation. METHODS: This is an open-label, nonrandomized, single-center, pilot study of IVM observation in subjects with solid tumors undergoing surgical resection. RESULTS: This study was active on January 1, 2019 (NCT03823144) and funded by the Mayo Clinic Florida Cancer Focused Research Team Award. As of September 27, 2020, we had enrolled 20 patients. Accrual period is expected to end by December 31, 2021. CONCLUSIONS: This trial will support the development of interventions to improve patient treatment by extending the application of IVM to the tumor microenvironment. IVM observations during volume and pressor management at the time of surgery may aid in the development of strategies to augment responses to systemic treatments. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/15677.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".