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 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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".