A112 THE EDMONTON PANCREATICOBILIARY INFLAMMATION AND CANCER (EPIC) PROGRAM – A NEW MULTIDISCIPLINARY COORDINATION OF CARE INITIATIVE
Bibliographic record
Abstract
Abstract Background Pancreaticobiliary (PB) cancer is associated with a poor prognosis. A timely diagnosis and a coordinated, multidisciplinary effort may improve outcomes as patients navigate the healthcare system for investigations and treatment. Aims To establish and pilot a multidisciplinary (gastroenterology [GI], hepatopancreatic biliary [HPB] surgery, radiology, oncology, palliative medicine, and nutrition) collaborative care pathway (CCP) to coordinate timely referral and access to management for patients with PB cancer. Methods With multidisciplinary expert input, a CCP for PB cancer patients was developed. A donation to our hospital foundation facilitated the hiring of a nurse navigator (NN) to coordinate this process. The EPIC program was initiated as a 6-month pilot project on 01/07/23 for all consecutive patients referred to the University of Alberta Hospital with suspected PB cancer. Referrals were triaged by GI and HPB surgery into resectable, borderline resectable, or unresectable arms of the CCP. EUS biopsy and/or ERCP for stent were performed as needed. A referral to medical oncology was made after a diagnosis was established. Descriptive statistics were completed. Results At 3 months, 64 patients (39 M, 25 F), mean age 67±12 years (range 19-95 years), were referred to EPIC. Presenting symptoms were abdominal pain (73%), weight loss (40%), jaundice (27%), nausea/vomiting (21%), itching (8%), and new-onset diabetes (5%). Site of cancer was pancreas (77%, 49/64), bile duct (20%, 13/64), and ampulla (3%, 2/64). Timelines to care access were compared with a similar pre-CCP patient cohort as shown in Table. Conclusions The role of the NN is to reduce variability and coordinate timely referral, scheduling, and follow-up based on the care pathway, and to serve as a point of contact for ongoing patient issues. The EPIC program pilot is on track to meet the timelines set forth in the care pathway. Primary care outreach to increase awareness of symptoms and for prompt investigation may further improve access to care in these patients. Note: negative numbers indicate days where the GI/HPB review occurred before the CT/MRI, or intervention before GI/HPB review EPIC COLLABORATIVE CARE PATHWAY Funding Agencies None
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".