Impact of an inter-professional clinic on pancreatic cancer outcomes: The Princess Margaret Cancer Centre (PM) experience.
Bibliographic record
Abstract
444 Background: Patients with pancreatic ductal adenocarcinoma (PDAC) have limited treatment options. Management of complex symptoms and psychosocial implications requires an interprofessional approach as prognosis is often measured in months. A multidisciplinary approach has been associated with improvement in clinical outcomes including survival. We aimed to evaluate the impact of an inter-professional approach for PDAC patients at the Wallace McCain Centre for Pancreatic Cancer (WMCPC) at PM on their management and clinical outcomes. Methods: We undertook retrospective review of all patients with PDAC seen at PM two years before (July ‘12 – June ‘14) and two years after (July ‘14 – June ‘16) establishment of the WMCPC. Standard therapies (surgical approach, chemotherapy, radiation therapy) were the same during both time periods. Comparison of overall survival (OS), stage at diagnosis, surgical outcomes, waiting times, and proportion seen by social worker, dietician and clinical nurse specialist (CNS) was explored with descriptive statistic and survival analysis. Results: A total of 993 patients were reviewed; 482 patients pre- and 511 patients post-WMCPC. Age (median 67 yrs), sex (54% men) and stage III/IV (52%) were similar in both groups. There was a trend to improved OS in the post-WMCPC group (9.6 vs. 10.9 m; p = 0.055); multivariable analysis found a significant improvement in OS after adjustment for performance status and stage (p = 0.023; HR 0.84, 95% CI 0.72-0.98). Rate of R0 versus R1/R2 resection for curative surgery (n = 264, 28%) was similar in both groups. Time from referral to first clinic visit significantly decreased from 13.4 to 8.8 days in the post-WMCPC group (p < 0.001) as did time from first clinic appointment to diagnostic biopsy (25.9 vs. 16.9 days, p = 0.022). Patients in the post-WMCPC were more frequently seen by a social worker, dietician or CNS (8% vs. 38%, 9% vs. 35% and 31% vs. 50% respectively, p < 0.001). Conclusions: Establishment of an interprofessional clinic for the treatment of PDAC patients at PM has streamlined diagnosis, aided symptom management and improved overall survival. This has implications for planning care delivery models and proves the value of this intervention.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".