Indocyanine green near-infrared fluorescence imaging shows promise for intraoperative fluorescence of parathyroid tissue in dogs
Bibliographic record
Abstract
Objective: To evaluate IV indocyanine green (ICG) near-infrared fluorescence (NIRF) imaging to identify normal canine parathyroid tissue. Methods: A cumulative effect study followed by a dose evaluation study with 8 purpose-bred dogs was performed from February through April 2023. Dogs were randomized to receive IV ICG at 0.2, 0.3, or 0.4 mg/kg after the thyroid and parathyroid glands were exposed. A NIRF endoscope positioned 8 cm above the thyroid-parathyroid complex obtained images. Subjective and objective measures of fluorescence were recorded and compared for the thyroid gland, external parathyroid gland, and internal parathyroid gland. Results: Repeated ICG administration did not affect time to peak fluorescence but increased peak parathyroid gland fluorescence. Subjective fluorescence scores of the parathyroid glands were significantly higher in monochromatic modality compared to other ICG-NIRF modalities. Initial fluorescence was immediate for all glands. Mean time to peak objective fluorescence was 0.2 to 1.9 minutes. Higher ICG doses generally had higher peak fluorescence than lower ICG doses. Indocyanine green-NIRF did not consistently distinguish normal parathyroid glands from thyroid tissue. Conclusions: ICG-NIRF at 0.2 to 0.4 mg/kg effectively fluoresces normal parathyroid glands in dogs, although the subjective fluorescence achieved in the parathyroid glands is similar to fluorescence in the adjacent thyroid glands. Parathyroid fluorescence varied substantially between ICG-NIRF modality, with the highest fluorescence observed in the monochromatic modality. Clinical Relevance: ICG-NIRF may aid in intraoperative localization of parathyroid glands, particularly the identification of ectopic parathyroid tissue. Further evaluation of ICG-NIRF for the identification of pathologic parathyroid tissue in clinical patients is indicated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".