Efficacy and Safety of neoadjuvant tislelizumab plus chemotherapy for the Treatment of non-small cell lung cancer: A Single-Arm Meta-Analysis Among Chinese Patients
Bibliographic record
Abstract
extracted characteristics were summarized as following: authors, publication year, nation, sample size, therapeutic regimen, median age and reported endpoints.Indexes for clinical and safety outcomes included ORR, R0 resection, MPR, pCR, the incidence of any AEs and ≥grade 3 AEs.Also, two investigators independently assessed and extracted the required data from all included studies. Main outcome(s) Overall response rate(ORR), Major Pathological response (MPR), Pathological complete response (pCR), R0 resection rate. Additional outcome(s) Adverse events (AEs).Quality assessment / Risk of bias analysis The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of including non-controlled trials.The retrospective studies were assessed by JBI Critical Appraisal Checklist for Case Series. Strategy of data synthesisAll data in this metaanalysis were analyzed with R 4.1.2software.Heterogeneity was measured using the Chi-square test and I2 statistic.P < 0.1 indicated a statistically significant difference.If significant heterogeneity (P-value 50%) existed, random-effect model was performed.Otherwise, the fixed-effects model was used.Potential publication bias was accessed by Begg's and Egger's tests.The stability of the results was assessed by sensitivity analysis. Subgroup analysis No.Sensitivity analysis Sensitivity analysis was performed to analyze the stability and reliability of the pooled results. Language restriction English. Country(ies) involved China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".