Immunohistochemical expression of ki-67 in odontogenic keratocyst: Evidence of aggressive behavior.
Bibliographic record
Abstract
The odontogenic keratocyst (OKC) well-known for its aggressiveness and high recurrence rate, comprises approximately 11% of all jaw cysts. Due to its aggressive behavior it was placed into category of tumour in 2005 by the World Health Organization (WHO). Objectives: The purpose of this study was to determine the Ki-67 expression in Odontogenic Keratocysts to predict its proliferative potential. Study Design: Descriptive study. Setting: Department of Morbid Anatomy and Histopathology, UHS. Periods: June 2014- June 2018. Material & Methods: This is a descriptive study comprising of 39 cases of odontogenic cysts. These surgically removed samples were processed at University of Health Sciences (UHS) laboratory. Routine staining with Hematoxylin & Eosin stain along with immunohistochemistry (IHC) with Ki-67 antibody was performed. Immunohisto chemical scoring was done on the basis of percentage of the nuclear staining of Ki-67. Data was entered into SPSS 22 and descriptive statistics were measured in the form of percentage and frequency. Quantitative variables such as age of patient, size of the cyst, and Ki-67 score were also measured. P value <0.05 was taken as significant. Results: The mean age of the patients was 25.08 ±14.5 years. Significant association was observed between histological variables with odontogenic keratocyst such as parakeratinized epithelial lining (p = 0.00), epithelial hyperplasia both typical and atypical (p = 0.02) and focal spongiosis (p = 0.04). Foci having epithelial atypia demonstrated stronger staining intensity compared to adjacent normal epithelium. However, no significant association was observed between the histological variables and Ki-67 expression. Conclusion: OKC expressed low Ki-67 expression in most of the cases, however, foci of strong expression were also observed in few cases.
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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 | 0.001 |
| 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) | 0.001 | 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".