Clinicopathological and prognostic significance of platelet count in patients with ovarian cancer
Bibliographic record
Abstract
Aim: Increasing evidence indicates that platelet count is a useful biomarker of long-term outcomes in patients with ovarian cancer. However, the prognostic value of platelet count in patients with ovarian cancer remains controversial. We therefore conducted a meta-analysis aimed to investigate the prognostic role of the platelet count in patients with ovarian cancer. Method: A comprehensive search was performed from the databases of PubMed, Embase and the Cochrane Library until June 20, 2017. A total of 18 studies with 6754 patients were included. Hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) and odds ratios and 95% CIs from each study were pooled. Results: The results demonstrated that elevated pretreatment platelet count was significantly related to poor survival from ovarian cancer; the pooled HRs for overall, progression-free and disease-free survival were 1.81 (95% CI 1.52–2.15), 1.48 (95% CI 1.24–1.75) and 1.39 (95% CI 1.19–1.61), respectively. Subgroup analyses were divided by ethnicity, sample size, FIGO stage, cut-off value of the platelet count, analysis method and Newcastle Ottawa Scale score, but the results did not show any significant change in the main results. Increased platelet count was also significantly associated with the FIGO stage, tumor differentiation, ascites, residual tumor mass, CA125 level, recurrence and metastasis. Conclusion: This meta-analysis revealed that an elevated platelet count pretreatment denotes a predictive factor of poor prognosis and unfavorable clinicopathological parameters for ovarian cancer patients.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".