Fluorescence-guided resection of intracranial VX2 tumor in a preclinical model using 5-aminolevulinic acid (ALA): preliminary results
Bibliographic record
Abstract
Fluorescence-guided brain tumor resection may help the neurosurgeon to identify tumor margins that merge imperceptibly into the normal brain tissue and are difficult to identify under white light illumination even using an operating microscope. We compared the amount of residual tumor after white light resection using an operating microscope versus that after fluorescnece-guided resection of an intracranial VX2 tumor in a preclinical model using our previously developed co-axial fluorscence imaging and spectroscopy system, exciting and detecting PpIX fluorescence at 405nm and 635nm respectively. Preliminary results: No fluorescence was present in 3 non-tumor-bearing animals. Fluorescence was present in all 15 tumor-bearing animals after white light resection was completed. To date in 4 rabbits, a decrease in residual tumor was found when using additional fluorescence guided resection compared to white light resection only. Conclusions: ALA induced PpIX fluorescence detects tumor margins not seen under an operation microscope using while light. Using fluorescence imaging to guide tumor resection resulted in a 3-fold decrease in the amount of residual timor. However, these preliminary results indicate that also an additional amount of normal brain is resected, which will be further investigated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".