Identifying Factors Influencing Pancreatic Cancer Management to Inform Quality Improvement Efforts and Future Research
Bibliographic record
Abstract
Pancreatic cancer (PC) patients appear to receive suboptimal care. We conducted a systematic review to identify factors that influence PC management which are amenable to quality improvement. MEDLINE, EMBASE, and the references of eligible studies were searched from 1996 to July 2014. Two authors independently selected and reviewed eligible studies. Identified factors were mapped onto a framework of determinants of care delivery and outcomes. Methodological quality of studies was assessed using Downs and Black criteria. Most of the 33 eligible studies were population-based observational studies conducted in the United States. Patient (age, socioeconomic status, race) and institutional (case volume, academic status) factors influence care delivery and outcomes (complications, mortality, readmission, survival). Two studies implemented interventions to improve quality of care (centralization to high-volume hospitals, multidisciplinary care). One study examined system determinants (referral wait times). No studies examined the influence of guideline or provider characteristics. The overall lack of health services research in PC is striking. Factors and interventions identified here can be used to plan PC quality improvement programs. Further research is needed to explore the influence of guideline and provider factors on PC management and evaluate the impact of quality improvement interventions.
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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 | 0.065 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".