A review of p53 expression and mutation in human benign, low malignant potential, and invasive epithelial ovarian tumors
Bibliographic record
Abstract
BACKGROUND: In the current study, the authors present pooled data from studies that investigated p53 protein expression and/or mutation in human epithelial ovarian tumors. METHODS: The English literature in the MEDLINE, PubMed, and Ingenta databases was searched to the end of the year 2000 to identify relevant studies. Data were pooled across eligible studies, and the prevalence of p53 expression and mutation among benign, low malignant potential (LMP), and invasive tumors was determined. Prevalence estimates by tumor histology, International Federation of Gynecology and Obstetrics (FIGO) stage, and grade also were calculated. RESULTS: The pooled prevalence estimate for p53 overexpression among epithelial ovarian carcinomas was 51% (95% confidence intervals [95% CI], 50-53%) compared with 17% (95% CI, 15-20%) among LMP tumors and 7% (95% CI, 5-10%) among benign tumors. p53 mutation prevalence estimates were 45% (95% CI, 42-47%), 5% (95% CI, 2-9%), and 1% (95% CI, 0-5%), respectively, for invasive, LMP, and benign tumors. The prevalence of these p53 abnormalities was found to be associated positively with increasing tumor grade and stage. Differences based on histologic subtype also were found. CONCLUSIONS: Although these pooled estimates might appear to offer support for various hypotheses regarding the role of p53 in ovarian carcinoma, the limitations inherent in these data hamper the interpretation of the significance of any of the findings. Future studies will require innovative methods to address the limitations of many previous investigations and more comprehensive investigation into defective tumor suppression mechanisms.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".