Endoclip Magnetic Resonance Imaging Screening: A Local Practice Review
Bibliographic record
Abstract
PURPOSE: Not all endoscopically placed clips (endoclips) are magnetic resonance imaging (MRI) compatible. At many institutions, endoclip screening is part of the pre-MRI screening process. Our objective is to determine the contribution of each step of this endoclip screening protocol in determining a patient's endoclip status at our institution. METHODS: A retrospective review of patients' endoscopic histories on general MRI screening forms for patients scanned during a 40-day period was performed to assess the percentage of patients that require endoclip screening at our institution. Following this, a prospective evaluation of 614 patients' endoclip screening determined the percentage of these patients ultimately exposed to each step in the protocol (exposure), and the percentage of patients whose endoclip status was determined with reasonable certainty by each step (determination). RESULTS: Exposure and determination values for each step were calculated as follows (exposure, determination): verbal interview (100%, 86%), review of past available imaging (14%, 36%), review of endoscopy report (9%, 57%), and new abdominal radiograph (4%, 96%), or CT (0.2%, 100%) for evaluation of potential endoclips. Only 1 patient did not receive MRI because of screening (in situ gastrointestinal endoclip identified). CONCLUSIONS: Verbal interview is invaluable to endoclip screening, clearing 86% of patients with minimal monetary and time investment. Conversely, the limited availability of endoscopy reports and relevant past imaging somewhat restricts the determination rates of these. New imaging (radiograph or computed tomography) is required <5% of the time, and although costly and associated with patient irradiation, has excellent determination rates (above 96%) when needed.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".