The Diagnostic Yield of Magnetic Resonance Cholangiopancreatography in the Setting of Acute Pancreaticobiliary Disease — A Single Center Experience
Bibliographic record
Abstract
PURPOSE: To discern whether preceding ultrasound (US) results, patient demographics and biochemical markers can be implemented as predictors of an abnormal Magnetic Resonance Cholangiopancreatography (MRCP) study in the context of acute pancreaticobiliary disease. METHODS: A retrospective study was performed assessing US results, age, gender, elevated lipase and biliary enzymes for consecutive patients who underwent an urgent MRCP following an initial US for acute pancreaticobiliary disease between January 2017-December 2018. Multivariable binary logistic regression models were constructed to assess for predictors of clinically significant MRCPs, and discrepant US/MRCP results. RESULTS: < 0.05) were found to be independent predictors for a subsequent abnormal MRCP. Contrarily, gender and elevated biliary enzymes were not reliable predictors of an abnormal MRCP, or significant MRCP/US discrepancies. Of 66 cases (43%) of discordant US/MRCPs, half had clinically significant discrepant findings such as newly discovered choledocholithiasis and pancreaticobiliary neoplasia. Age was the sole predictor for a significant US/MRCP discrepancy, with 2% increase in the odds of a significant discrepancy per year of increase in age. CONCLUSION: An abnormal US, hyperlipasemia and increased age serve as predictors for a subsequent abnormal MRCP, as opposed to gender and biliary enzyme elevation. Age was the sole predictor of a significant US/MRCP discrepancy that provided new information which significantly impacted subsequent management. In the remaining cases, however, MRCP proved useful in reaffirming the clinical diagnosis and avoiding further investigations.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".