Preoperative platelet count as an independent predictor of long‐term outcomes among patients undergoing resection for intrahepatic cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: An elevated platelet count may reflect neoplastic and inflammatory states, with cytokine-driven overproduction of platelets. The objective of this study was to evaluate the prognostic utility of high platelet count among patients undergoing curative-intent liver surgery for intrahepatic cholangiocarcinoma (ICC). METHODS: /L. The relationship between preoperative platelet count, cancer-specific survival (CSS), and overall survival (OS) was examined. RESULTS: Among 825 patients undergoing curative-intent resection for ICC, 139 had a high platelet count, which correlated with multifocal disease, lymph nodes metastasis, poor to undifferentiated grade, and microvascular invasion. Patients with high platelet counts had worse 5-year (35.8% vs. 46.7%, p = 0.009) CSS and OS (24.8% vs. 39.8%, p < 0.001), relative to patients with a low platelet count. After controlling for relevant clinicopathologic factors, high platelet count remained an adverse independent predictor of CSS (HR = 1.46, 95% CI 1.02-2.09) and OS (HR = 1.59, 95% CI 1.14-2.22). CONCLUSIONS: High platelet count was associated with worse tumor characteristics and poor long-term CSS and OS. Platelet count represents a readily-available laboratory value that may preoperatively improve risk-stratification of patients undergoing curative-intent liver resection for ICC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".