Carotid endarterectomy for elderly patients: predicting complications. Annals of Internal Medicine
Bibliographic record
Abstract
Objective: To determine whether the complication or death rate from carotid endarterectomy can be predicted from hospital and physician structural variables, such as the hospital's teaching status or the number of endarterectomies done by the surgeon per year. Design: Survey of medical records. After controlling for the severity of the patient's condition on the basis of data in the medical record at the time of the endarterectomy, regression analyses were used to predict the postoperative stroke, heart attack, and 30-day death rate as a function of patient, physician, and hospital char-acteristics. Setting: Three geographic areas (states or large parts of states; average population, 3 million) in the United States. Patients: Random sample of 1302 patients 65 years of age or older having carotid endarterectomy in 1981. Intervention: Carotid endarterectomy. Measurements and Main Results: Of 1302 patients, 11.3 % had a postoperative stroke or heart attack or died within 30 days of the operation. Patient age, race, income, and gender; physician vol-ume, board certification status, and age; and hospital size, for-profit status, ownership, and teaching status were not significantly related to the postoperative complication or death rate. If the surgeon was a graduate of a foreign, but not a Western European or Canadian, medical school, however, the average complication or death rate rose from 10.4 % to 19.6 % (P < 0.05). Conclusions: The effectiveness of carotid endarterectomy de-pends heavily on its complication rate. Because complications after surgery cannot, in general, be predicted from structural variables, referring physicians cannot rely solely on the surgeon's experience and qualifications when recommending a carotid endarterectomy. The surgeon's and the hospital's actual postoperative complication and death rate should be considered.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".