Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: According to the American Cancer Society, despite being one of the most preventable forms of cancer, colorectal cancer (CRC) is the fourth most common type of cancer and is the second leading cause of cancer-related deaths in the United States (US). The 2020 Behavioral Risk Factor Surveillance System estimates that around one quarter of the US population aged 50 to 75 and over one half of those in their early 50s remain unscreened despite being at risk. In 2016, the United State Food and Drug Administration approved a blood test for CRC screening called Epi proColon. Epi proColon is intended for patients who are at risk of developing CRC and have declined other existing forms of screening. As such, it could help increase CRC screening among at-risk patients who would otherwise not adhere to screening recommendations.\nObjective: The objective of the study was to determine predictors of Epi proColon utilization among insured patients aged 50 to 55 with no previous history of CRC screening, diagnosis, or total colectomy.\nMethods: A matched case-control study was conducted using de-identified data from the ClinformaticsTM® DataMart, an administrative health claims database. Medical claims were used to identify cases of Epi proColon utilization in 2017. Using risk-set sampling, cases were randomly matched to 10 controls based on the index month of each case. Patients were excluded from the study cohort if they had previously been screened for CRC, were diagnosed with CRC, had history of a total colectomy, did not have a recent preventive health visit, or did not meet continuous enrollment criteria. A final study population was 935 patients, 85 cases and 850 controls. Significant predictors (age, race/ethnicity, income level, education level, and geographic region) of Epi proColon utilization were determined using multivariable logistic regression analysis to calculate odds ratios (OR) and corresponding 95% confidence intervals (CI).\nResults: We identified 5 demographic and socioeconomic characteristics that were statistically significant predictors of Epi proColon utilization. Age was found to increase the likelihood of Epi proColon utilization by 1.174 times for each increasing year of age (95% CI 1.023 – 1.349, p-value = 0.0228). Patients who were identified as Hispanic were 2.019 times more likely to use Epi proColon when compared to those identified as White (95% CI 1.030 – 3.960, p-value = 0.409). Patients with high school education or less were 1.818 times more likely to use Epi proColon when compared to those with some college education or greater (95% CI 1.098 – 3.010, p-value = 0.0201). Patients with household incomes of less than $40,000 annually were 2.187 times more likely to use Epi proColon when compared to those with household incomes $40,000 or greater (95% CI 1.209 – 3.958, p-value = 0.0097). Compared with patients from the Northwest, Midwest, or with unknown geographic regions, patients from the South were 3.070 times more likely (95% CI 1.619 – 5.821, p-value = 0.0006) and patients from the West were 2.340 times more likely (95% CI 1.098 – 4.987, p-value = 0.0276) to use Epi proColon.\nConclusion: The findings from this study suggest that Epi proColon may help to increase CRC screening rates in Hispanic patients, low-income patients, and those with less education. As such, it could be used to improve CRC outcomes among these groups that have higher rates of non-adherence to CRC screening recommendations. Increasing age was also predictive of utilization. This information can be helpful for public health officials, providers, and advocacy groups in developing strategies to address disparities in CRC screening and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it