A Histopathologic Review of Undiagnosed Neoplasms in 205 Evisceration Specimens
Bibliographic record
Abstract
PURPOSE: Evisceration and enucleation are 2 ophthalmic surgeries used to treat blind and painful eyes. The benefits of evisceration over enucleation are many but this procedure is contraindicated in cases of a suspected intra-ocular mass. Given the lack of such studies in the literature, our aim was to review a large sample of eviscerated specimens to document the prevalence of unexpected neoplasms. METHODS: During the study period (1994-2011), 13,591 human ophthalmic specimens were received at the Henry C. Witelson Ophthalmic Pathology Laboratory and Registry. Of those, 205 were evisceration specimens. Histopathologic reports were reviewed to retrieve relevant clinical information that included clinical diagnosis, age, gender, laterality, and final histopathologic diagnosis. RESULTS: The total number of unexpected neoplasms was 4 (1.95%) including 2 (0.97%) malignancies: necrotic melanoma (1), ciliary body adenoma (1), iris nevus (1), and spindle cell melanoma (1). All the other remaining eyes had histopathologic findings that were consistent with the underlying diagnosis. CONCLUSIONS: With proper pre-operative evaluation, including history, ophthalmologic examination, and imaging studies, the rate of unexpected neoplasms in evisceration specimens were low. Evisceration is a simpler and cheaper procedure when compared with enucleation, and our results corroborate its safety, whenever indicated to treat blind and/or painful eyes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".