Characterization of Near-Infrared Imaging and Indocyanine-Green Use Amongst General Surgeons: A Survey of 263 General Surgeons
Bibliographic record
Abstract
BACKGROUND: Near-infrared fluorescence imaging (NIRFI) is an increasingly utilized imaging modality, however its use amongst general surgeons and its barriers to adoption have not yet been characterized. METHODS: This survey was sent to Canadian Association of General Surgeons and the Society of American Gastrointestinal and Endoscopic Surgeons members. Survey development occurred through consensus of NIRFI experienced surgeons. RESULTS: Survey completion rate for those opening the email was 16.0% (n = 263). Most respondents had used NIRFI (n = 161, 61.2%). Training, higher volumes, and bariatric, thoracic, or foregut subspecialty were associated with use (P < .001).Common reasons for NIRFI included anastomotic assessment (n = 117, 72.7%), cholangiography (n = 106, 65.8%), macroscopic angiography (n = 66, 41.0%), and bowel viability assessment (n = 101, 62.7%). Technical knowledge, training and poor evidence were cited as common barriers to NIRFI adoption. CONCLUSIONS: NIRFI use is common with high case volume, bariatric, foregut, and thoracic surgery practices associated with adoption. Barriers to use appear to be lack of awareness, low confidence in current evidence, and inadequate training. High quality randomized studies evaluating NIRFI are needed to improve confidence in current evidence; if deemed beneficial, training will be imperative for NIRFI adoption.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".