Autofluorescence-Guided Surgery in the Management of Osteonecrosis of the Jaw: Correlation Between Bone Autofluorescence and Histopathological Findings in 56 Samples
Bibliographic record
Abstract
(1) Background: Osteonecrosis of the jaw (ONJ) remains a challenging condition without a universally accepted treatment protocol. Surgical therapy, particularly Er:YAG laser-assisted surgery, has shown more predictable long-term results compared with non-surgical options. However, the identification of resection margins in ONJ surgery is complex and currently relies on the surgeon’s intraoperative assessment, without standardization. Bone autofluorescence (AF) has been proposed as an intraoperative diagnostic tool for visualizing necrotic bone; under VELscope (LED Medical Diagnostics Inc., Barnaby, BC, Canada) illumination, healthy bone exhibits hyperfluorescence, while pathological bone appears dark brown/black (loss of autofluorescence, LAF). (2) Methods: 22 patients with ONJ requiring surgical therapy were included. After bone exposure, VELscope system was used to induce and visualize bone AF. Areas exhibiting absent or pale AF were identified as necrotic and removed; additional samples were collected from adjacent hyperfluorescent regions. (3) Results: Histopathologic evaluation of 56 specimens were conducted; 35 hypofluorescent samples were found to be necrotic bone tissue; in the 21 hyperfluorescent samples, 86% demonstrated normal, vital bone. The correlation between fluorescence and bone vitality was highly significant (p < 0.0000001). (4) Conclusions: Our data show that AF-guided surgical resection, combined with Er:YAG laser-assisted surgery, may improve clinical outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".