Chemotherapy choice in advanced pancreatic cancer: What patient and disease factors influence prescription patterns?
Bibliographic record
Abstract
327 Background: FOLFIRINOX (FFX), gemcitabine+nab-paclitaxel (GnP) and gemcitabine monotherapy (Gem)) are universally funded as first-line chemotherapy regimens for advanced pancreatic cancer (APC) in Ontario, Canada. However, there is scarce real-world data on factors that may influence choice of chemotherapy regimens in APC. Methods: Patients who received first-line chemotherapy for APC between April 2015-March 2016 in Ontario were identified from CCO’s New Drug Funding Program database and linked to the Ontario Cancer Registry and other provincial databases to ascertain baseline factors. Multinomial logistic regressions were used to examine the associations between the prescribed chemotherapy regimen and baseline factors. Results: 546 patients were identified, with a mean age of 65 and 43.6% female. 9.9% and 9.7% had received adjuvant gemcitabine and radiation treatment respectively. 17.6% had previous pancreatic resection. 68.3% had zero Charlson score and 30.6% had ECOG performance status (PS) of 0. 72.7% had metastatic disease. The majority of the patients received FFX (52.4%) compared to GnP (35.7%) and Gem (11.9%). Age and ECOG PS were strongly associated with choice of chemotherapy regimens. (See Table) Conclusions: In Ontario, increased patient age and worse ECOG PS are strongly associated with choice of Gem compared to GnP and FFX. Previous treatments and stage of disease also impact chemotherapy choice. Understanding how providers choose chemotherapy in APC aids in comprehending our practices. Odds ratio (OR) and p value from multinomial logistic regressions. [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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".