It's Big Surgery
Bibliographic record
Abstract
In Brief Objective: To identify the processes, surgeons use to establish patient buy-in to postoperative treatments. Background: Surgeons generally believe they confirm the patient's commitment to an operation and all ensuing postoperative care, before surgery. How surgeons get buy-in and whether patients participate in this agreement is unknown. Methods: We used purposive sampling to identify 3 surgeons from different subspecialties who routinely perform high-risk operations at each of 3 distinct medical centers (Toronto, Ontario; Boston, Massachusetts; Madison, Wisconsin). We recorded preoperative conversations with 3 to 7 patients facing high-risk surgery with each surgeon (n = 48) and used content analysis to analyze each preoperative conversation inductively. Results: Surgeons conveyed the gravity of high-risk operations to patients by emphasizing the operation is “big surgery” and that a decision to proceed invoked a serious commitment for both the surgeon and the patient. Surgeons were frank about the potential for serious complications and the need for intensive care. They rarely discussed the use of prolonged life-supporting treatment, and patients' questions were primarily confined to logistic or technical concerns. Surgeons regularly proceeded through the conversation in a manner that suggested they believed buy-in was achieved, but this agreement was rarely forged explicitly. Conclusions: Surgeons who perform high-risk operations communicate the risks of surgery and express their commitment to the patient's survival. However, they rarely discuss prolonged life-supporting treatments explicitly and patients do not discuss their preferences. It is not possible to determine patients' desires for prolonged postoperative life support on the basis of these preoperative conversations alone. We observed surgeons discussing high-risk operations to identify the processes used to establish a preoperative agreement about postoperative treatments. Although surgeons go to great lengths to describe the serious nature of high-risk operations, they do not regularly discuss the use of prolonged life support and patients do not explicitly agree to participate in prolonged aggressive treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".