Impact of homologous recombination deficiency biomarkers on outcomes in patients with early breast cancer: a systematic review protocol
Bibliographic record
Abstract
Introduction Patients with breast cancer with homologous recombination deficiency (HRD) such as germline BRCA1/2 mutations would respond to DNA-damaging drugs. Several clinical studies have revealed that HRD biomarkers were associated with the outcomes of patients with early breast cancer (EBC). However, no systematic review has determined the prognostic role of HRD biomarkers in patients with EBC. Therefore, this study will systematically combine and analyse the results of previous studies, to facilitate the clinical use of HRD detection in EBC. Methods and analysis We will search five databases including PubMed, Cochrane Library, EMBASE, OVID and Web of Science through December 2021, with no language restriction. Two reviewers will independently screen all records based on pre-established inclusion and exclusion criteria. The main outcomes include pathological complete response, disease-free survival and Ooerall survival. In addition, all studies included must contain the detection of HRD score, HRD status or HRD-related gene mutational status and protein expression. Data extraction will be carried out by two reviewers independently according to a self-designed template. The Newcastle-Ottawa Quality Assessment Scale and Jadad Scale will be used for quality assessment for cohort studies and randomised clinical trials, respectively. Review Manager V.5.3.5 will be used to perform meta-analysis. Both the Q test and I 2 statistic will be used to assess heterogeneity. Subgroup and sensitivity analyses will be conducted if significant heterogeneity appears and cannot be reduced by using a random-effect model. Ethics and dissemination Ethical approval is not required for a systematic review. The results will be disseminated through international and national conferences or peer-reviewed publications. PROSPERO registration number CRD42021286522.
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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 | 0.047 | 0.049 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.015 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 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".