Low rates of specialized cancer consultation and cancer-directed therapy for noncurable pancreatic adenocarcinoma: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Although advancements in systemic therapy have improved the outlook for pancreatic adenocarcinoma, it is not known if patients get access to these therapies. We aimed to examine the patterns and factors associated with access to specialized cancer consultations and subsequent receipt of cancer-directed therapy for patients with non-curative pancreatic adenocarcinoma. METHODS: We conducted a population-based analysis of noncurative pancreatic adenocarcinoma diagnosed over 2005-2016 in Ontario by linking administrative health care data sets. Our primary outcomes were specialized cancer consultation and receipt of cancer-directed therapy (chemotherapy or a combination of chemo- and radiation therapy [chemoradiation therapy]). We examined specialized cancer consultation with hepato-pancreatico-biliary surgery, medical and radiation oncology. We used multivariable logistic regression to identify factors associated with medical oncology consultation and cancer-directed therapy. RESULTS: Of 10 881 patients, 64.9% had a consultation with specialists in medical oncology, 35.1% with hepatopancreatico-biliary surgery and 24.7% with radiation oncology. Sociodemographic characteristics were not associated with the likelihood of medical oncology consultation. Of these patients, 4144 received cancer-directed therapy, representing 38.1% of all patients and 58.6% of those who consulted with medical oncology. Of 6737 patients not receiving cancer-directed therapy, 2988 (44.4%) had a consultation with medical oncology. Older age and lowest income quintile were independently associated with lower likelihood of cancer-directed therapy. If the first specialized cancer consultation was with medical or radiation oncology, the likelihood of cancer-directed therapy was significantly higher compared with surgery. INTERPRETATION: A considerable proportion of patients with noncurable pancreatic adenocarcinoma in Ontario did not have a specialized cancer consultation and most did not receive cancer-directed therapy. We identified disparities in specialized cancer consultation and receipt of systemic cancer-directed therapy that indicate potential gaps in assessment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".