The association between SPINK1 and clinical outcomes in patients with prostate cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract: Evidence of the prognostic role of serine peptidase inhibitor Kazal type 1 (SPINK1) in prostate cancer (PCa) is controversial. The aim of this study was, therefore, to evaluate the association between SPINK1 and clinical outcomes in PCa. Searches were made of PubMed, Medline, Embase, and the China Biology Medicine disc (CBMdisc) up to January 2017. The Newcastle–Ottawa Scale was used to assess the risk of bias of included studies. RevMan software was used to perform meta-analysis, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was employed for assessing the quality of the evidence. Ten studies with 17,161 patients were included in the analysis. Random-effect models were adopted for all outcomes with significant heterogeneities. In patients treated with radical prostatectomy, SPINK1 was associated with biochemical recurrence (BCR) (hazard ratio [HR] =1.41, 95% confidence interval [CI]: 1.01–1.97; P =0.04), but not PCa-specific mortality (HR =0.93, 95% CI: 0.33–2.57; P =0.88), and overall survival (OS) (HR =0.89, 95% CI: 0.58–1.35; P =0.57). In metastatic PCa, SPINK1 was significantly associated with castration-resistant PCa-free survival (HR =3.87, 95% CI: 1.87–8.00; P =0.0003) and OS (HR =2.59, 95% CI: 1.16–5.78; P =0.02). However, the quality of the evidence was very low for all study outcome measures. In conclusion, although SPINK1 was not a predictor of PCa mortality or OS among patients who underwent radical prostatectomy, it may have prognostic value in metastatic PCa. Keywords: SPINK1, clinical outcomes, prostate cancer, meta-analysis, systematic review
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".