Does regional variation impact decision-making in the management and palliation of pancreatic head adenocarcinoma? Results from an international survey
Bibliographic record
Abstract
BACKGROUND: Management and palliation of pancreatic head adenocarcinoma is challenging. End-of-life decision-making is a variable process involving multiple factors. METHODS: We conducted a qualitative, physician-based, 40-question international survey characterizing the impact of medical, religious, social, training and system factors on care. RESULTS: A total of 258 international clinicians completed the survey. Respondents were typically fellowship-trained (78%), with a mean of 16 years' experience in a university-affiliated (93%) hepato-pancreato-biliary group (96%) practice. Most (91%) believed resection is potentially curative. Most patients were discussed preoperatively by multidisciplinary teams (94%) and medical assessment clinics (68%), but rarely critical care (21%). Intraoperative surgical palliation included double bypass or no intervention for locally advanced nonresectable tumours (41% and 49% v. 14% and 85%, respectively, for patients with hepatic metastases). Postoperative admission to the intensive care unit was frequent (58%). Severe postoperative complications were often treated with aggressive cardiopulmonary resuscitation, intubation and critical care (96%), with no defined time points for futility (74%). Admitting surgeons guided most end-of-life decisions (97%). Formal medical futility laws were rarely available (26%). Insurance status did not alter treatment (97%) or palliation (95%) in non-universal care regions. Clinician experience, regional culture and training background impacted treatment (all p < 0.05). CONCLUSION: Despite remarkable overall agreement, geographic and training differences are evident in the treatment and palliation of pancreatic head adenocarcinoma.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".