Clinicopathological correlation of caruncular lesions: a 22-year report from the Middle East
Bibliographic record
Abstract
INTRODUCTION: Caruncular lesions are uncommon and diverse, making accurate clinical diagnosis challenging. Discrepancies between clinical and histopathological diagnoses are frequent, and malignant lesions can metastasize early. The lack of substantial regional data necessitates a detailed study of the clinical and histopathological characteristics of these lesions. METHODS: A retrospective study was conducted over a 22-year period, including 52 patients with caruncular lesions. Clinical presentations, demographic data, and histopathological findings were recorded. All lesions underwent biopsy and histological examination to correlate clinical and pathological diagnoses. RESULTS: A total of 52 patients with caruncular lesions were included, with a mean age of 48 years. The majority presented with unilateral lesions, and six patients had bilateral involvement. The most common presenting complaints were pigmented or enlarging masses. Histopathological examination revealed 13 distinct lesion types, with inflammatory lesions (25%) and melanocytic tumors (23%) being the most common. Malignant lesions were identified in 11.5% of cases. The clinicopathological correlation was accurate in 23% of cases. CONCLUSION: Caruncular lesions present significant diagnostic challenges due to their rarity and histopathological diversity. This study underscores the importance of histopathological examination for accurate diagnosis and highlights the need for regional data to better understand the epidemiology of these lesions. The findings also suggest that while most lesions are benign, a high index of suspicion for malignancy should be maintained, particularly in cases of rapid growth or atypical presentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".