ERCP in Patients With Sickle Cell Disease: Diagnostic and Therapeutic Dilemmas
Bibliographic record
Abstract
BACKGROUND: Cholestatic jaundice (CJ) in patients with sickle cell disease (SCD) poses diagnostic and therapeutic dilemmas. This is an evaluation of the role of ERCP in SCD. METHODS: A total of 224 SCD patients with CJ had ERCP. The indications for ERCP were based on clinical and biochemical evidence of CJ and ultrasound findings. RESULTS: The indications were: CJ only in79, CJ and dilated ducts in 103, and CJ and biliary stones in 42. The ERCP findings were: (A) For those with CJ only: ERCP was normal in 45, showed dilated ducts with no stones in 13, dilated ducts with stones in 16, normal CBD with a stone in 1; (B) For those with CJ, dilated ducts: ERCP was normal in 17, showed dilated ducts with stones in 47, dilated ducts without stones in 28, normal CBD with a stone in 1, a choledochoduodenal fistula in 2; (C) For those with CJ and duct stones: ERCP was normal in 2, showed dilated ducts with stones in 21, dilated ducts without stones in 14, normal CBD with a stone in 1. CONCLUSIONS: ERCP was unnecessary in a significant number (27%) of patients. This is especially so for those with CJ only (57%). These should be evaluated further prior to ERCP. There was also a significant number (19%) who had ES for duct dilatation without an obstruction. The reason for this dilatation is not known and the value of ES in this group needs to be investigated further.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".