KI-67 IN PITUITARY NEOPLASMS
Bibliographic record
Abstract
KI-67, A MARKER of cellular proliferation, has been studied extensively in pituitary neoplasia. It is of relevance to various clinicopathological parameters, including tumor subtype, size, invasiveness, and recurrence, as well as patient age and sex. Generally, pituitary tumors behaving aggressively have increased Ki-67 labeling indices. Nonetheless, there is considerable overlap in Ki-67 labeling between noninvasive and invasive adenomas as well as between adenomas and pituitary carcinomas. Not only is there no general agreement regarding the relationship of Ki-67 labeling index and tumor invasiveness, but the same is also true of the association with pituitary tumor size, growth fraction, and recurrence. Whereas a number of studies found conclusive associations of Ki-67 labeling indices with aggressive behavior, size, and/or adenoma subtype, others fail to do so. It is evident that discrepant data regarding tumor behavior in part has its basis in nonuniform study criteria. For example, different investigators use varying criteria of tumor invasion and recurrence. Herein, we review the literature relating Ki-67 expression and various other clinicopathological parameters and conclude that uniform definitions and methods, as well as new markers, are key to improved treatment of pituitary tumors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".