Low Platelet Count Predicts Reduced Survival in Potentially Resectable Hepatocellular Carcinoma
Bibliographic record
Abstract
The prognostic role of platelet count in hepatocellular carcinoma (HCC) remains unclear, and in fact both thrombocytopenia and thrombocytosis are reported as predictors of unfavourable outcomes. This study aimed to clarify the prognostic value of preoperative platelet count in potentially resectable HCC. We retrospectively reviewed 128 patients who underwent hepatic resection for HCC at a tertiary academic centre (2007−2019). Patient data were modelled by regression analysis, and platelet count was treated as a continuous variable. 89 patients had BCLC 0/A tumours and 39 had BCLC B tumours. Platelet count was higher in patients with larger tumours and lower in patients with higher MELD scores, advanced fibrosis, and portal hypertension (p < 0.001 for all listed variables). After adjusting for BCLC stage and tumour diameter, low platelet count associated with reduced overall survival (hazard ratio 1.25 per 50/nL decrease in platelet count, 95% confidence interval (CI) 1.02−1.53, p = 0.034) and increased perioperative mortality (odds ratio 1.96 per 50/nL decrease in platelet count, 95% CI 1.19−3.53, p = 0.014). Overall, low platelet count correlates with increased liver disease severity, inferior survival, and excess perioperative mortality in resectable HCC. These insights might be applied in clinical practice to better select patients for resection.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".