Patients’ perceptions of palliative surgical procedures: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Patients with incurable malignancies can require surgical intervention. We prospectively evaluated patients treated with palliative surgery to qualitatively assess peri-operative outcomes. METHODS: Eligible patients were assessed at a tertiary care cancer center. Demographic information and peri-operative morbidity and mortality were collected. Semi-structured qualitative interviews were obtained pre-operatively and post-operatively (1 month). Qualitative evaluation was performed using content analysis and an inductive approach. RESULTS: Twenty-eight patients were approached and 20 consented to interview. Data saturation was achieved after 14 patients. Median patient age was 58% and 56% were female. Peri-operative morbidity and mortality were 44% and 22%, respectively. "No other option" was seen as a dominant pre-operative theme (14 of 18). Other pre-operative themes included a "poor understanding of prognosis and the role of surgery in overall treatment plan". Post-operative themes included a "perceived benefit from surgery" and "satisfaction with decision-making", notwithstanding significant complications. Improved understanding of prognosis and the role of surgery were described post-operatively. CONCLUSIONS: Despite limited options and a poor understanding of prognosis, many patients perceived benefit from palliative surgery. However, peri-operative mortality was substantial. A robust and thorough patient-centered discussion about individual goals for surgery should be undertaken by surgeon, patient and family prior to embarking on a palliative operation.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".