Development and evaluation of a connective tissue phantom model for subsurface visualization of cancers requiring wide local excision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wide local excision (WLE) of tumors with negative margins remains a challenge because surgeons cannot directly visualize the mass. Fluorescence-guided surgery (FGS) may improve surgical accuracy; however, conventional methods with direct surface tumor visualization are not immediately applicable, and properties of tissues surrounding the cancer must be considered. We developed a phantom model for sarcoma resection with the near-infrared fluorophore IRDye 800CW and used it to iteratively define the properties of connective tissues that typically surround sarcoma tumors. We then tested the ability of a blinded surgeon to resect fluorescent tumor-simulating inclusions with ∼1-cm margins using predetermined target fluorescence intensities and a Solaris open-air fluorescence imaging system. In connective tissue-simulating phantoms, fluorescence intensity decreased with increasing blood concentration and increased with increasing intralipid concentrations. Fluorescent inclusions could be resolved at ≥1-cm depth in all inclusion concentrations and sizes tested. When inclusion depth was held constant, fluorescence intensity decreased with decreasing volume. Using targeted fluorescence intensities, a blinded surgeon was able to successfully excise inclusions with ∼1-cm margins from fat- and muscle-simulating phantoms with inclusion-to-background contrast ratios as low as 2∶1. Indirect, subsurface FGS is a promising tool for surgical resection of cancers requiring WLE.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 it