Landscape of Health-Related Quality of Life in Patients With Early-Stage Pancreatic Cancer Receiving Adjuvant or Neoadjuvant Chemotherapy
Bibliographic record
Abstract
OBJECTIVES: Pancreatic resection is associated with postoperative morbidity and reduced quality of life (QoL). A systematic literature review was conducted to understand the patient-reported outcome measure (PROM) landscape in early-stage pancreatic cancer (PC). METHODS: Databases/registries (through January 24, 2019) and conference abstracts (2014-2017) were searched. Study quality was assessed using the Newcastle-Ottawa Scale/Cochrane risk-of-bias tool. Searches were for general (resectable PC, adjuvant/neoadjuvant, QoL) and supplemental studies (resectable PC, European Organisation for Research and Treatment of Cancer QoL Questionnaire [QLQ] - Pancreatic Cancer [PAN26]). RESULTS: Of 750 studies identified, 39 (general, 22; supplemental, 17) were eligible: 32 used QLQ Core 30 (C30) and/or QLQ-PAN26, and 15 used other PROMs. Baseline QLQ-C30 global health status/QoL scores in early-stage PC were similar to all-stage PC reference values but lower than all-stage-all-cancer values. The QoL declined after surgery, recovered to baseline in 3 to 6 months, and then generally stabilized. A minimally important difference (MID) of 10 was commonly used for QLQ-C30 but was not established for QLQ-PAN26. CONCLUSIONS: In early-stage PC, QLQ-C30 and QLQ-PAN26 are the most commonly used PROMs. Baseline QLQ-C30 global health status/QoL scores suggested a high humanistic burden. Immediately after surgery, QoL declined but seemed stable over the longer term. The QLQ-C30 MID may elucidate the clinical impact of treatment on QoL; MID for QLQ-PAN26 needs to be established.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".