Prognostic value of lipid profiles after radical prostatectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Lipid profiles are believed to play an important role in the tumorigenesis and progression of prostate cancer (PCa), but research combining those data is lacking. Therefore, this meta-analysis aims to assess the prognostic role of lipid profiles after RP. METHOD: We systematically searched PubMed, Embase, and Cochrane Library Central Register of Controlled Trials for articles evaluating association between lipid profiles and prognosis after RP. Odds ratio (OR) and hazard ratio (HR) of lipid profiles for advanced pathological tumor features and biochemical recurrence (BCR) were extracted and pooled OR and HR were calculated. Newcastle-Ottawa scale was used for study quality assessment and funnel plot was used for evaluating publication bias. RESULTS: Twelve articles involving 11,108 patients were eventually selected. We found low HDL was associated with more frequent occurrence of pathological T stage (pT) ≥ T3 (pooled OR = 1.29, 95% CI 1.07-1.56) and Gleason score (GS) ≥8 (pooled OR = 1.32, 95% CI 1.02-1.72) after RP. Hypertriglyceridemia was also linked with higher risk of pT ≥ T3 (pooled OR = 1.20, 95% CI 1.01-1.42) and positive surgical margin (PSM) (pooled OR = 1.36, 95% CI 1.11-1.65). However, no significant association was observed between BCR and abnormal lipid profile levels. CONCLUSION: Low HDL level was associated with more common occurrence of pT ≥ T3 and GS ≥8, and elevated triglycerides level was linked higher risk of pT ≥ T3 and PSM, but none of the lipid subfractions was correlated with biochemical recurrence after RP.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".