Patient-reported Limitations to Surgical Buy-in
Bibliographic record
Abstract
OBJECTIVE: To characterize how patients buy-in to treatments beyond the operating room and what limits they would place on additional life-supporting treatments. BACKGROUND: During a high-risk operation, surgeons generally assume that patients buy-in to life-supporting interventions that might be necessary postoperatively. How patients understand this agreement and their willingness to participate in additional treatment is unknown. METHODS: We purposively sampled surgeons in Toronto, Ontario, Boston, Massachusetts, and Madison, Wisconsin, who are good communicators and routinely perform high-risk operations. We audio-recorded their conversations with patients considering high-risk surgery. For patients who were then scheduled for surgery, we performed open-ended preoperative and postoperative interviews. We used directed qualitative content analysis to analyze the interviews and surgeon visits, specifically evaluating the content about the use of postoperative life support. RESULTS: We recorded 43 patients' conversations with surgeons, 34 preoperative, and 27 postoperative interviews. Patients expressed trust in their surgeon to make decisions about additional treatments if a serious complication occurred, yet expressed a preference for significant treatment limitations that were not discussed with their surgeon preoperatively. Patients valued the existence or creation of an advance directive preoperatively, but they did not discuss this directive with their surgeon. Instead they assumed it would be effective if needed and that family members knew their wishes. CONCLUSIONS: Patients implicitly trust their surgeons to treat postoperative complications as they arise. Although patients may buy-in to some additional postoperative interventions, they hold a broad range of preferences for treatment limitations that were not discussed with the surgeon preoperatively.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".