Validation of a semi-quantitative scoring system and workflow for analysis of fluorescence quantification in companion animals
Bibliographic record
Abstract
Significance Many commercially available near-infrared (NIR) fluorescence imaging systems lack algorithms for real-time quantifiable fluorescence data. Creation of a workflow for clinical assessment and post hoc analysis may provide clinical researchers with a method for intraoperative fluorescence quantification to improve objective outcome measures. Aim Scoring systems and verified image analysis are employed to determine the amount and intensity of fluorescence within surgical specimens both intra and postoperatively. Approach Lymph nodes from canine cancer patients were obtained during lymph node extirpation following peritumoral injection of indocyanine green (ICG). First, a semi-quantitative assessment of surface fluorescence was evaluated. Images obtained with a NIR exoscope were analysed to determine fluorescence thresholds and measure fluorescence amount and intensity. Results Post hoc fluorescence quantification (threshold of Hue = 165–180, Intensity = 30–255) displayed strong agreement with semi-quantitative scoring (k = 0.9734, p < 0.0001). Fluorescence intensity with either threshold of 35–255 or 45–255 were significant predictors of fluorescence and had high sensitivity and specificity (p < 0.05). Fluorescence intensity and quantification had a strong association (p < 0.001). Conclusion The validation of the semi-quantitative scoring system by image analysis provides a method for objective in situ observation of tissue fluorescence. The utilization of thresholding for ICG fluorescence intensity allows post hoc quantification of fluorescence when not built into the imaging system.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".