Analysis of PD-L1 Expression in Breast Cancer: A Systematic Review and Meta-Analysis in Asian Population
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the level of PD-L1 protein expression in patients with BCs who were of Asian descent. METHODS: Three databases were conducted on this article up to August 10th, 2022. The reference lists of the publications were examined for further studies, and in cases of duplicates, a study with a larger sample size was added. In survival analysis, the hazard ratio (HR) was applied to the circumstances characterized by the frequency of occurrences, and for the clinicopathological characteristic, the best-adjusted odds ratio (OR) with a 95% confidence interval (CI) was employed. The Newcastle-Ottawa Scale (NOS) was utilized to evaluate selection criteria, comparison, and exposure to establish the quality of the technique in the under-consideration studies. The Z test determined the association analysis of OS, DFS, and clinicopathological characteristics with PD-L1 expression. RESULT: All eight trials for OS and six for DFS were considered, with 4.111 and 3.071 participants, respectively. Overexpression of PD-L1 was linked to a reduced OS compared to individuals with undetectable expression (HR= 1.58, 95% CI 1.04-2.40; P=0.03). We analyzed clinicopathological features, and it elevated in individuals with histological grade III (OR=2.39, 95% CI 1.26-4.54; P=0.008) and positive node (OR=0.68, 95% CI 0.48-0.97; P<0.05). CONCLUSION: Overexpression of PD-L1 was associated with a shorter OS in BCs patients. High PDL1 was higher in persons with nodal positivity and histological grade III.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".