Longitudinal non-invasive optical biopsy of keratinocyte cancers to monitor efficacy and response to treatment
Bibliographic record
Abstract
Due to the impracticality of performing serial biopsies for the same lesion following treatments, efficacy data for low-risk keratinocyte skin cancer treatments is currently lacking. In this study, we employed non-invasive optical sectioning technology to monitor cellular dynamics and tissue architectural modifications post treatments every three months for up to a year. This optical sectioning was achieved utilizing a multimodal microscopy, integrating reflectance confocal microscopy (RCM), two-photon fluorescent (TPF) microscopy, and second harmonic generation (SHG) microscopy. Patients diagnosed with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and actinic keratosis (AK) were recruited in the study. Treatments evaluated included curettage and electrodessication (C&D), surgical excision, photodynamic therapy (PDT), cryotherapy, and topical chemo/immunotherapy.
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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 | 0.001 | 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) | 0.001 | 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".