Value of p53 sequencing in the prognostication of head and neck cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract This review aimed to examine the relationship between TP53 mutational status, as determined by genomic sequencing, and survival in squamous cell carcinoma of the head and neck. The databases Medline, Embase, Web of Science (core collection), Scopus and Cochrane Library were searched from inception to April 2021 for studies assessing P53 status and survival. Qualitative analysis was carried out using the REMARK criteria. A meta-analyses was performed and statistical analysis was carried out to test the stability and reliability of results. Twenty-five studies met the inclusion criteria, of which fifteen provided enough data for quantitative evaluation. TP53 mutation was associated with worse overall survival (HR 1.75 [95%CI 1.45–2.10], p < 0.001), disease-specific survival (HR 4.23 [95%CI 1.19–15.06], p = 0.03), and disease-free survival (HR 1.80 [95%CI 1.28-; 2.53], p < 0.001). Qualitative assessment identified room for improvement and the pooled analysis of all anatomical subsites leads to heterogeneity that may erode the validity of the observed overall effect and its subsequent extrapolation and application to individual patients. Our systematic review and meta-analysis supports the utility of TP53 mutational as a prognostic factor for survival in head and neck squamous cell carcinoma. A well designed prospective, multi-centre trial is needed to definitively answer this question Value of p53 sequencing in the prognostication of head and neck cancer: a systematic review and meta-analysis
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| 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".