Abstract A89: Gender and racial disparities in non-small cell lung cancer: A systematic review
Bibliographic record
Abstract
Abstract Lung cancer is the leading cause of cancer death in the U.S. and remains the least-funded cancer in North America. Many studies addressed disparities in lung cancer incidence rate among the races; however, few studies described the effect of both race and gender in the risk of manifesting this disease. These studies lack consensus for both the etiology and the magnitude of gender-based disparities and their impact on disease development on specific racial groups. These unknown race-associated characteristics between men and women might adversely affect the improvement of current therapies and patient outcomes. Characterizing gender as a possible risk modifier in lung cancer development among certain ethnic groups may aid in the development of targeted agents that improve patient outcomes. This study aimed to investigate the global pattern of gender-based disparities in incidence of lung cancer and describe the etiologic factors associated with different racial groups in non-small cell lung cancer (NSCLC), since it comprises 85% of all diagnosed cases. This aim was accomplished by analyzing data scrutinized using a systematic review approach for studies published between 1996 and 2016. We found a statistically significant effect of race on lung cancer incidence rate, and this effect varies by gender. We also found that certain races are more prone to develop adenocarcinoma in their histology than others, regardless of their sex. In addition, we found that Asian women have higher rates of NSCLC regardless of their living environment. Our findings also show that the number of cases of NSCLC is rising among women of all races, regardless of their smoking status. By visualizing NSCLC pattern in both men and women among different countries and racial groups, we stimulate future findings to establish efficacious preventative strategies and will stimulate research advancements toward sex- and racial-designed diagnostics, including alternative treatments that could reduce the manifestation of this disease. Note: This abstract was not presented at the conference. Citation Format: Noor Alsaadoun, Karen Kopciuk, Desiree Hao, Karl Riabowol, D. Morley Hollenberg, D. Gwyn Bebb. Gender and racial disparities in non-small cell lung cancer: A systematic review [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr A89.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".