2002 An observational study of the effectiveness of FIT test as a risk stratification tool in frail patients presenting with anaemia
Bibliographic record
Abstract
Abstract Introduction Faecal-immunochemical-test is employed as a screening tool for colorectal cancer. Our observational study examined the FIT in primary care as a risk stratification tool in frail patients. Method The records of 217 frail patients over a 24-month period were analysed. Patients with haematological indices of anaemia were offered FIT to detect GI haemorrhage as part of assessment for selection for lower GI investigations. Patients were risk stratified based on FIT results based on the presence or absence of red flags. Patients who were FIT positive were referred for urgent lower GI endoscopy versus those who were FIT negative were managed without bowel investigations unless there were red flags such as abdominal mass, changed bowel habits or family history of bowel cancer. Results Of 217 patients over a 24-month period of these 42 patients (19.4%) were FIT positive. All of these (n = 42) underwent colonoscopy of which 15 (normal)16 (colonic polyps) 6 (diverticulosis) 3 (colorectal cancer). Of the 42 FIT positive patients 16 were on direct oral anticoagulant (DOAC). Patients on DOACs and those on dual anti platelet agents were more likely to be FIT positive. We also found a positive correlation between higher frailty indices, HAS BLED scores and chronic kidney disease and low creatinine clearance r=0.82, p=0.001. Despite the small numbers in this study the correlation is statistically significant Conclusion There is a statistically significant positive correlation of FIT positive and frailty indices with DOACs, Dual anti platelet agents, CKD, low creatinine clearance (r=0.82 and p=0.001). Following this the HASBLED scores increased hence our practices implemented an enhanced surveillance of monitoring these patients quarterly due to the increased risk. We advocate frailty indices should be incorporated in the HAS BLED scores for improved patient safety.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".