ASSESSING SURGEONS’ DISCLOSURE OF RISK INFORMATION BEFORE CAROTID ENDARTERECTOMY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To make an informed decision about treatment, patients need accurate information about the benefits and risks of treatment and 'non-treatment' options. A survey was conducted to determine patients' recall of the extent and effect of preoperative disclosure by surgeons to patients of risks about carotid endarterectomy (CEA). METHODS: A self-administered questionnaire was given to 133 patients undergoing elective CEA in New South Wales. The primary outcome measures were patient recall of preoperative discussion, self-assessed estimates of stroke risk with and without surgery and receipt of written information before CEA. RESULTS: A significantly higher proportion of patients recalled that their surgeon discussed the short-term stroke risk (i.e. within 30 days) if they decided to undergo CEA (86.2%) than if they decided not to have the procedure (76.9%) (P = 0.04). Of those patients who recalled the surgeon discussing their short-term stroke risk with CEA, only 24 (18.0%) were accurately able to quantify this risk. Patients were significantly more likely to recall their surgeon discussing their long-term stroke risk (i.e. within 2 years) if they decided not to have CEA (72.4%) than if they decided to have the CEA (31.5%) (P < 0.0001). CONCLUSIONS: Patients recalled discussions with their surgeon about short-term stroke risk. Only a minority, however, accurately quantified their postoperative stroke risk. In view of variable patient recall, decision aids could assist.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it