Inequities Associated With Advanced Stage at Presentation of Head and Neck Cancer
Bibliographic record
Abstract
Importance: Social determinants of health (SDoH) are defined by a wide range of factors (eg, built environment, economic stability, education level, discrimination, racism, access to health care). Advanced stage at presentation or delayed diagnosis heavily influences health outcomes in patients with head and neck cancer (HNC). While the drivers of advanced-stage presentation come from a multitude of sources, SDoH plays an outsized role. Objective: To systematically review the published literature to identify which SDoH are established as risk factors for delayed diagnosis or advanced stage at presentation among patients with HNC. Evidence Review: In this systematic review, a literature search of PubMed, Web of Science, and Embase was conducted on February 27, 2023, using keywords related to advanced stage at presentation and delayed diagnosis of HNC between 2013 and 2023. Quality assessment was evaluated through the Newcastle-Ottawa Scale. Articles were included if they focused on US-based populations and factors associated with advanced stage at presentation or delayed diagnosis of HNC. Findings: Overall, 50 articles were included for full-text extraction, of which 30 (60%) were database studies. Race was the most commonly reported variable (46 studies [92%]), with Black race (43 studies [93%]) being the most studied racial group showing an increased risk of delay in diagnosis of HNC. Other commonly studied variables that were associated with advanced stage at presentation included sex and gender (41 studies [82%]), insurance status (25 studies [50%]), geographic region (5 studies [10%]), and socioeconomic status (20 studies [40%]). Male sex, lack of insurance, rurality, and low socioeconomic status were all identified as risk factors for advanced stage at presentation. Conclusions and Relevance: This systematic review provides a comprehensive list of factors that were associated with advanced HNC stage at presentation. Future studies should focus on evaluating interventions aimed at addressing the SDoH in communities experiencing disparities to provide a net positive effect on HNC care.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".