Clinicopathological comparison of benign eyelid tumours in tertiary care centers in Lima and Montreal from 2010 to 2019
Bibliographic record
Abstract
OBJECTIVE: The eyelids are complex structures composed of various tissues. As such, a wide array of lesions exist that affect the eyelids. Clinical diagnosis of these lesions is essential to differentiate benign from malignant ones. Although accurate, some cases are misdiagnosed. Histopathology remains the crucial step in the final diagnosis of eyelid lesions. The objective of the study is to describe and compare the clinicopathological diagnosis of benign eyelid tumours between two tertiary care centers, one in South America and the other in North America, from 2010 to 2019. METHODS: We reviewed 1 935 tumours at the McGill University Health Centre (MUHC)-McGill University Ocular Pathology and Translational Research Laboratory and 1 256 tumours collected at "Dr. Jose Antonio Avendaño Valdez" Ocular Pathology at the Instituto Nacional de Oftalmologia Peru (INO) (2010-2019). Demographic information and clinical and histopathological diagnoses were collected in an anonymized fashion. RESULTS: The average age was 61.04 years at MUHC and 47.44 years at INO, benign tumours accounted for 78.11 % and 79.84%, respectively. The three most frequent benign tumour were squamous cell papilloma, nevus, and seborrheic keratosis at both sites. Lesions most commonly affected the upper and lower eyelid. 2.32% at the MUHC and 1.35% at the INO of cases were clinically misdiagnosed as benign tumours but confirmed malignant by histopathology. CONCLUSION: The most common lesions were squamous cell papilloma, nevus, and seborrheic keratosis, although the most common diagnosis varied, likely due to environmental factors differing at each site. Histopathology is essential for the accurate diagnosis of eyelid tumours.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".