Histopathological analysis of juvenile patients with melanocytic lesions of the conjunctiva
Bibliographic record
Abstract
Objective: This study aimed to examine the frequency of atypical features in conjunctival nevi in pediatric patients referred to the McGill University Health Center-McGill University Ocular Pathology and Translational Research Laboratory and to emphasize the importance of histopathological analysis to rule out malignant lesions, such as melanoma. Methods: Forty-four pediatric patients younger than 20 years of age previously diagnosed with melanocytic lesions of the conjunctiva were included in this study, and the database was analyzed for a 10-year period (2006–2015). Clinical information such as age, gender, location, type, size, and agreement between clinical and pathological diagnosis was also recorded and presented as means and percentages. Results: The mean age was 11.3 years, of which only 9.1% were older than 18 years. Gender predilection was found toward males (52.3%). An agreement was noted between clinical and pathological diagnosis in 77.2% of cases; only 22.72% showed atypia upon histopathological examination. The most common pathological diagnosis was compound nevus with different characteristics. The atypical lesions included conjunctival melanoma, junctional nevi with atypia, compound nevi with atypia, and cystic compound nevi. Moreover, 70% of the patients with atypical lesions were males and 60% of the specimens were from the right eye. Conclusion: An impressive number of 30% of all patients had some type of atypia after histopathological analysis. Therefore, based on these results, it is of extreme importance that all melanocytic lesions are sent for histopathological analysis, so an accurate diagnosis can be established.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".