The Predictive Value of CK19 and CD99 in Pancreatic Endocrine Tumors
Bibliographic record
Abstract
Prediction of behavior in pancreatic endocrine tumors (PETs) is reliant on clinicopathologic features. However, there remains a cohort of PETs that behave aggressively despite showing indolent pathologic features. Recently, it has been suggested that CK19 and CD99 are sensitive ancillary markers that predict outcome in PETs. An analysis of 54 PETs and 2 resected liver metastases was undertaken to examine the relationship of CK19 and CD99 and the pathologic criteria in the WHO classification of PETs. CK19 was found to correlate with mitotic count (>5/50 high-power fields), an MIB-1 labeling index of > or =2%, lymphovascular/perineural permeation, lymph node involvement, and liver spread. Although not statistically significant, CK19-negative tumors tended to be smaller than the average tumor size in the series (2.5 vs. 3.6 cm). CD99 did not show any significant correlation with any of the WHO criteria. Tumors that are confined to the pancreas with low mitotic count and MIB-1 labeling index, tended to be CD99-positive. Both CK 19 (negative) and CD99 (positive) correlated with insulin-positive PETs. In conclusion, CK 19 may prove to be a useful ancillary diagnostic test in the routine work-up of PETs. CD99 does not appear to be as useful. There is no compelling evidence, from our study, to suggest that both these markers may be used in concert to predict the behavior of PETs.
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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 | 0.007 |
| 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 | 0.001 | 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".