Bibliographic record
Abstract
T he current issue of the Journal includes a study by Narula et al (1) (pages 421-426) that nicely demonstrates the numerous reasons why fecal occult blood test (FOBT) use outside of screening purposes should be considered inappropriate and how it may, in fact, negatively impact patient care.The authors performed a chart review of all FOBTs ordered in Hamilton Health Sciences' (Hamilton, Ontario) acute care campuses over a three-month period in 2011, combined with a survey of health care providers on their practices regarding the use of FOBT.Use of point-of-care FOBT was not included.The clinical presentations were anemia, overt or suspected gastrointestinal (GI) bleeding, diarrhea, iron deficiency and dyspepsia.Thirty-four percent of patients underwent ≥2 FOBTs, and the majority of tested patients were either on medications or on a diet that could interfere with the test.Only 50% of the FOBT positives were ever referred for a GI consultation and, most importantly, 27% of patients presenting with overt GI bleeding in whom an FOBT was ordered experienced a delay in the GI referral because of the FOBT process.The survey included 67 health care professionals (mainly primary care physicians and nurses); the most common reasons for ordering an FOBT were: symptoms potentially consistent with GI bleeding (84%); anemia (53%); iron deficiency with or without anemia (31%); overt GI blood loss (26%); and nonbloody diarrhea (10%).Interestingly, screening for colorectal cancer was a cited reason in only 25% of the cases.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".