Barriers to accessing healthcare services: A multidisciplinary approach towards improving pancreatic cancer survival in a Canadian province.
Bibliographic record
Abstract
e18018 Background: Pancreatic cancer (PC) is associated with the highest death rate among common malignancies and is the fourth leading cause of cancer-related death in North America. Despite similar access to treatment options across Canada, the province of Nova Scotia (NS) has the lowest 5-year survival rate for PC. To investigate reasons behind the poor PC outcomes in NS, a multidisciplinary team was created to investigate barriers to care and streamline patient flow. In 2016, initial data informed the reorganization of the hepatopancreaticobiliary (HPB) multidisciplinary team towards the goal of identifying and reducing barriers to care and, ultimately, improving survival. Methods: This quality improvement project included a retrospective chart review of PC patient data from a single institution (The NS Cancer Center), where over 80% of PC patients from this province are seen. A review of PC diagnosis, referrals patterns, and wait time data was undertaken. Results: Data was extracted on 365 patients with a diagnosis of PC between 2011 and 2014. During that period, only 40.4% of patients diagnosed with PC had a tissue diagnosis and just over 71% had a baseline CA19-9. Referral rate to Medical Oncology (MO) was 53%, mean wait time to see MO was 37.2 days and only 23% of patients received systemic treatment. Initiatives to improve access to care included standardization of diagnostic procedures, early triaging of referrals, transfer of port-a-cath (PAC) insertions from interventional radiology to the HPB surgeons, and the creation of provincial guidelines, which were implemented in 2016. Positive Improvements were observed in all identified barriers to care. Conclusions: Barriers to accessing care for PC patients in NS were identified, and a multidisciplinary team proposed provincial guidelines were implemented to expedite care. Preliminary results show improvement in all aspects of healthcare delivery. Survival data will be available in late 2019. [Table: see text]
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".