Geographic Information System to Determine Quality of Bowel Preparation for a Catchment Area of a Veterans Affairs Healthcare System: A Descriptive Analysis
Bibliographic record
Abstract
Introduction: Suboptimal bowel preparation can lead to missed polyps and cancers, incomplete exams, and repeat colonoscopy. Split dosing has improved preparation and adenoma detection, however, suboptimal preparations still continue. Barriers to suboptimal preparation have been evaluated but full understanding is still limited. The purpose of this study was to use the Geographic Information System (GIS) to visually represent the quality of bowel preparation in our hospital catchment area and further investigate barriers to suboptimal preparations. Methods: Retrospective chart review of outpatient colonoscopy reports at the Loma Linda Veterans Affairs Hospital (LLVAH) from March 2010 to March 2011 was analyzed. The Ottawa bowel preparation score was identified for each colonoscopy procedure that utilized a split dose preparation. A graduated code was created and was mapped using the Geographic Information System (GIS) software. A 25 and 40 mile buffer was created away from the LLVAH onto the counties that represent the catchment area. Results: A total of 1054 outpatient colonoscopy reports were evaluated between March 2010 and March 2011. All patients received a split dose preparation, and 50.9% participated in an educational class prior to colonoscopy. The average age was 63.2 years, 91.2% of the patients were male, and average BMI was 30. Significant co-morbidities included but were not restricted to diabetes, 35.2%, prior abdominal surgery, 37.6%, and psychiatric history, 47.2%. Bowel preparation scores were as follows, 17.3% excellent, 56.7% good, 18.7% fair, 5.8% poor, and 0.7% inadequate. GIS mapping revealed suboptimal preparations in areas of lower socioeconomic status around LLVAH. Conclusion: Suboptimal preparation can lead to missed polyps and colorectal cancers. It can also lead to repeat colonoscopy, which could further increase procedural risk to the patient as well as increase cost to both the patient and institution. GIS mapping allowed us to identify areas and populations of suboptimal bowel preparations. A geographical evaluation of high-risk populations of suboptimal preparation may be able to lead to more focused interventions that can lead to improved preparations for colonoscopy.Figure 1
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".