Gastroscopy Following a Positive Fecal Occult Blood Test and Negative Colonoscopy: Systematic Review and Guideline
Bibliographic record
Abstract
BACKGROUND: A sizeable number of individuals who participate in population-based colorectal cancer (CRC) screening programs and have a positive fecal occult blood test (FOBT) do not have an identifiable lesion found at colonoscopy to account for their positive FOBT screen. OBJECTIVE: To evaluate the evidence and provide recommendations regarding the use of routine esophagogastroduodenoscopy (EGD) to detect upper gastrointestinal (UGI) cancers in patients participating in a population-based CRC screening program who are FOBT positive and colonoscopy negative. METHODS: A systematic review was used to develop the evidentiary base and to inform the evidence-based recommendations provided. RESULTS: Nine studies identified a group of patients who were FOBT positive and colonoscopy negative. Three studies found no cases of UGI cancer. Four studies reported cases of UGI cancer; three found UGI cancer in 1% or less of the population studied, and one study found one case of UGI cancer that represented 7% of their small subgroup of FOBT-positive/colonoscopy-negative patients. Two studies did not provide outcome information that could be specifically related to the FOBT-positive/colonoscopy-negative subgroup. CONCLUSION: The current body of evidence is insufficient to recommend for or against routine EGD as a means of detecting gastric or esophageal cancers for patients who are FOBT positive/colonoscopy negative, in a population-based CRC screening program. The decision to perform EGD should be individualized and based on clinical judgement.
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 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 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".