Predictive Impact of Clinical Benefit in Chemotherapy-treated Advanced Pancreatic Cancer Patients in Northern Alberta
Bibliographic record
Abstract
OBJECTIVES: Patients with advanced pancreatic cancer (APC) have a poor prognosis and experience a large burden of disease-related symptoms. Despite advancements in the treatment of APC, survival is dismal and controlling disease-related symptoms and maintaining quality of life is paramount. We hypothesize that an improvement in disease-related symptoms, and therefore, a clinical benefit, while on chemotherapy is a predictive marker in APC. MATERIALS AND METHODS: Patients 18 and older with APC diagnosed between January 1, 2005 and December 31, 2010 and treated at the Cross Cancer Institute were identified using the provincial cancer registry. Disease symptoms were assessed at baseline and clinical benefit while on chemotherapy was defined using a composite endpoint of improvement in patient-reported pain, opioid consumption, Eastern Cooperative Oncology Group performance status, and/or weight. Best radiologic response, progression-free survival (PFS), and overall survival (OS) were recorded. RESULTS: Of 103 patients, the median age was 64, 58% were male and 66% had metastatic disease. At baseline, the majority of patients reported pain (80%), opioid use (61%), or weight loss (71%). In total, 35 (34%) patients received a clinical benefit with treatment but only 6 (17%) of these patients experienced a radiologic response. The median PFS and OS were improved in patients who experienced a clinical benefit (6.6 vs. 4.6 mo; P=0.03 and 11.7 vs. 6.1 mo; P<0.0001, respectively). CONCLUSIONS: In patients with APC treated with chemotherapy, experiencing a clinical benefit was associated with improved PFS and OS. However, it did not appear to correlate with radiologic response to chemotherapy. Prospective studies are warranted to further investigate the prognostic and predictive value of clinical benefit and improvement in quality of life as measured by standardized tools, in APC.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".