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Record W4407727153 · doi:10.2196/64809

Monthly Variations in Colorectal Cancer Screening Tests Among Federally Qualified Health Center Patients in Missouri: Quality Improvement Project

2025· article· en· W4407727153 on OpenAlexvenueno aff
Jane A. McElroy, Kevin D. Everett

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintColorectal cancerMedicineGerontologyQuality (philosophy)Environmental healthCancerInternal medicineComputer science

Abstract

fetched live from OpenAlex

Background: Cancer is the second leading cause of death in the United States. Compelling evidence shows screening detects colorectal cancer (CRC) at earlier stages and prevents the development of CRC through the removal of precancerous polyps. The Healthy People 2030 goal for CRC screening is 68.3%, but only 36.5% of Missouri federally qualified health center patients aged 50-75 years are up-to-date on CRC screening. For average risk patients, there are three commonly used screening tests in the United States-two types of stool tests collected at home (fecal immunochemical test [FIT]-immunochemical fecal occult blood test [FOBT] and FIT-DNA, such as Cologuard) and colonoscopies completed at procedural centers. Objective: This study aims to examine variation by month for the three types of CRC testing to evaluate consistent patient care by clinical staff. Methods: Data from 31 federally qualified health center clinics in Missouri from 2011 to 2023 were analyzed. A sample of 34,124 unique eligible "average risk" patients defined as persons not having a personal history of CRC or certain types of polyps, family history of CRC, personal history of inflammatory bowel disease, and personal history of receiving radiation to the abdomen or pelvic to treat a previous cancer or confirmed or suspected hereditary CRC syndrome. Another eligibility criterion is that patients need to be seen at least once at the clinic to be included in the denominator for the screening rate calculation. Descriptive statistics characterize the sample, while bivariate analyses assess differences in screening types by month. Results: Completion of CRC screening yielded statistically significant differences for patients completing the different types of CRC screening by month. October-January had the highest proportions of patients (644-680 per month, 8.5%-10.2%) receiving a colonoscopy, while February-April had the lowest (509-578 per month, 6.9%-7.8%), with 614 being the average monthly number of colonoscopies. For FIT-FOBT, June-August had the higher proportions of patients receiving this test (563-613 per month, 8.9%-9.6%), whereas December-February had the lowest (453-495 per month, 7.1%-8%), with 541 being the average monthly number of FIT-FOBT kits used. For FIT-DNA, March was the most popular month with 11.3% (n=261 per month) of patients using the Cologuard test, followed by April, May, and November (207-220 per month, 8.7%-9.4%), and January and June (168-171 per month, 7.2%-7.3%) had the lowest proportion of patients using Cologuard, with 193 being the average monthly number of FIT-DNA kits used. Combining all tests, February had the fewest CRC tests completed (1153/16,173, 7.1%). Conclusions: Home-based tests are becoming popular, replacing the gold standard colonoscopy, but need to be repeated more frequently. Monthly variation of screening over the course of a year suggests that CRC screening efforts and patient care may be less than ideal. Months with lower rates of screening for each type of CRC test represent opportunities for improving CRC screening.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.382
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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