The impact of brush cytology from endoscopic retrograde cholangiopancreatography (ERCP) on patient management at a UK teaching hospital
Bibliographic record
Abstract
INTRODUCTION: Patients with suspected pancreaticobiliary cancers frequently undergo endoscopic retrograde cholangiopancreatography (ERCP) to obtain brush cytology for confirmatory diagnosis. The outcome of this often leads to the management of the patient and can avoid more invasive investigations. There is a wide range of sensitivities and specificities reported in the literature. AIMS: To determine the accuracy of the brush cytology obtained at ERCP by performing a retrospective audit of all patients admitted to Guy's and St. Thomas' Hospital for ERCP during 2008-2013. Also, to evaluate the impact of cytology results on patient care following ERCP. METHOD: Data were collected from 4 January 2008 to 27 December 2013. This involved analysing EndoSoft (the in-house software for endoscopic data entry), Pathnet (the pathology database) and Electronic Patient Records. RESULTS: 162 patients underwent brush cytology during ERCP. 58 patients had positive cytology. With intention-to-treat analysis, sensitivity was 54.7%, specificity was 100.0% and negative predictive value was 53.9% with a positive predictive value of 100%. Patients with a positive brush cytology result required fewer investigations compared with patients with a negative cytology result. CONCLUSIONS: Our results compare favourably with previous studies in the field. Brush cytology has been ignored in recent times due to perceived poor results and efficacy. Our audit shows that it can reduce the number of investigations required to reach a diagnosis of malignancy and so is a valuable tool in the diagnosis of pancreaticobiliary malignancies. However, better guidance on preparation of samples for cytology is needed to reduce the number of insufficient samples.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".