Merged Near‐Infrared and White‐Light Imaging in Minimally Invasive Surgery
Bibliographic record
Abstract
Dear Editor, We read with interest the review article entitled ‘‘NearInfrared Fluorescence Imaging for Real-Time Intraoperative Anatomical Guidance in Minimally Invasive Surgery: A Systematic Review of the literature’’ by Schols et al. [1] Although we applaud the authors’ extensive literature review and thoughtful discussion, the authors incorrectly state that merged near-infrared (NIR) and whitelight (WL) image capabilities have not been incorporated into a laparoscopic or robotic platform. I respectfully refer them to a growing body of literature using the Pinpoint and Firefly systems (Novadaq Technologies, Mississauga, ON, Canada), both of which are designed specifically for merging NIR to WL images in laparoscopic and robotic surgery, respectively [2–4]. Merged WL and NIR imaging is the key to providing surgeons with truly augmented vision and the aforementioned commercially available systems very effectively allow for real-time minimally invasive surgery with a seamlessly superimposed fluorescence overlay.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".