Protocol: systematic review of the association between socio-economic status and survival in adult head and neck cancer
Bibliographic record
Abstract
BACKGROUND: Head and neck cancer incidence is increasing worldwide. Despite overall improvements in survival, numerous studies suggest worse survival in more disadvantaged populations; however, this literature has not been systematically reviewed. The aim of this review is to investigate whether lower compared to higher socioeconomic status (SES) influences survival in head and neck squamous cell cancer (HNSCC) and explore possible explanations for any relationship found. METHOD: A systematic strategy will be used to identify articles, appraise their quality and extract data. Online databases including MEDLINE, Web of Knowledge, ESBCO Host and Scopus will be used to locate observational studies of adults with a primary diagnosis of head and neck cancer in EU15+ countries (15 members of the EU, Australia, Canada, Norway, USA and New Zealand) where the outcomes report associations between SES and survival. This will be augmented by searching for grey literature and through reference lists. Data will be extracted using a standardised form. Study quality will be assessed using the Newcastle Ottawa scale and where possible meta-analysis of the pooled data will be conducted. DISCUSSION: This review will quantify the association between SES and survival outcomes for adult head and neck cancer patients in developed countries. The results will help identify gaps in the literature and therefore direct further novel research in the field. Ultimately, this will inform public policy and strategies to reduce the inequalities in HNSCC survival. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016037019 .
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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 | 0.077 | 0.122 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.011 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.007 |
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".