Oesophageal and Gastric Cancer: optimising care and outcomes in changing clinical practice: Slokdarm- en maagkanker : verbeteren van zorg en uitkomst in een veranderend zorglandschap
Bibliographic record
Abstract
Improving quality of care for patients with oesophageal and gastric cancer is a major challenge as the incidence is relatively low and most patients have an advanced disease at time of diagnosis. The studies presented in this thesis aimed to give more insight in the provided care and outcomes for patients with oesophageal and gastric cancer in daily clinical practice. It was shown that the hospital of diagnosis influences the probability to receive curative treatment for oesophageal and gastric cancer. Moreover, its impact on survival indicates that treatment decision-making may be improved for patients with these malignancies. It was also shown that survival improved for patients with oesophageal cancer the last 26 years, probably due to the introduction of neoadjuvant chemoradiotherapy and centralisation of surgery. Survival of patients with gastric cancer improved as well in the period after centralisation of surgery. Nevertheless, many gastric cancer patients that are eligible for perioperative treatment do not receive the adjuvant component of perioperative treatment or they do not receive chemotherapy at all in addition to surgery. Furthermore, survival remained stable for patients with metastatic gastric cancer, despite an increase in the use of palliative chemotherapy. To conclude, the studies in this thesis addressed several important challenges in diagnosis and treatment of oesophageal and gastric cancer.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".