Toluidine blue 1% eye drop versus optical coherence tomography for margin delimitation of ocular surface squamous neoplasia
Bibliographic record
Abstract
PURPOSE: To compare the use of toluidine blue 1% eye drops with anterior segment optical coherence tomography (OCT) for the determination of tumour margins in patients with ocular surface squamous neoplasia (OSSN). METHODS: The study was conducted from July 2020 to June 2021 at the Ocular Oncology department at the Federal University of São Paulo, Brazil. Slit-lamp photographs after toluidine blue staining and OCT of the anterior segment were taken on the same day from patients with OSSN. Photographs and OCT images were analyzed quantitatively using the software ImageJ and IMAGEnet®, respectively. The agreement between techniques was evaluated qualitatively through the Bland-Altman graph and quantitatively through intraclass correlation (ICC). RESULTS: A total of 21 participants (71.43% males) with a clinical diagnosis of OSSN were included in the study. The average + SD diameter along the chosen axes was 4.43 ± 2.08 mm with OCT of 4.37 ± 2.03 mm with toluidine blue, a difference not statistically significant (p = 0.2891). The Bland-Altman analysis indicated a good qualitative agreement between the methods, with all cases inserted within the limits of agreement from -0.3217 to 0.4268. The ICC quantitative analysis showed an almost perfect agreement of 99.57% (95%CI: 98.96-99.83%; p < 0.001). CONCLUSIONS: Our findings showed that OCT and toluidine eye drops are equivalent in determining margins for tumour measurements, which is particularly relevant in low-income settings where anterior segment OCT is not available. The use of toluidine blue 1% could be an useful alternative to quantify the size of the tumour, help to monitor tumour growth, and outline margins for surgical planning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".