Peer Review #3 of "Prognostic and clinicopathological role of RACK1 for cancer patients: a systematic review and meta-analysis (v0.1)"
Bibliographic record
Abstract
Background:The receptor for activated C kinase 1 (RACK1) expression is associated with clinicopathological characteristics and the prognosis of various cancers; however, the conclusions are controversial.As a result, this study aimed to explore the clinicopathological and prognostic values of RACK1 expression in patients with cancer.Methodology: PubMed, Embase, Web of Science, Cochrane Library, and Scopus were comprehensively explored from their inception to April 20, 2023, for selecting studies on the clinicopathological and prognostic role of RACK1 in patients with cancer that met the criteria for inclusion in this review.Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were used to assess the prognosis-predictive value of RACK1 expression, while pooled odds ratios (ORs) and 95% CIs were used to evaluate the correlation between RACK1 expression and the clinicopathological characteristics of patients with cancer.The quality of the included studies was evaluated using the Newcastle-Ottawa Scale. Results:Twenty-two studies (13 on prognosis and 20 on clinicopathological characteristics) were included in this systematic review and meta-analysis.The findings indicated that high RACK1 expression was significantly associated with poor overall survival (HR = 1.62; 95% CI, 1.13-2.33;P = 0.009; I 2 = 89%) and reversely correlated with disease-free survival/recurrence-free survival (HR = 1.87; 95% CI, 1.22-2.88;P = 0.004; I 2 = 0%).Furthermore, increased RACK1 expression was significantly associated with lymphatic invasion/N+ stage (OR = 1.74; 95% CI, 1.04-2.90;P = 0.04; I 2 = 79%) of tumors.Conclusions: RACK1 may be a global predictive marker of poor prognosis in patients with cancer and unfavorable clinicopathological characteristics.However, further clinical studies are required to validate these findings.
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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 | 0.026 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.020 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.005 |
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".