ASSESSING SURGEONS’ DISCLOSURE OF RISK INFORMATION BEFORE CAROTID ENDARTERECTOMY
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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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".