Is there a role for referral of high-risk patients seen in preoperative medical consultation for postoperative inpatient follow-up?
Bibliographic record
Abstract
The potential benefit of referring select high-risk surgical patients who are seen during a preoperative medical consultation for postoperative inpatient medical follow-up is uncertain. Over a seven-year period, our internal medicine perioperative clinic referred 5% of 4642 preoperative consults for postoperative follow-up. A retrospective chart review found that although reasons for referral were heterogeneous, those assessed by the medical consult team postoperatively were more comorbid, had more adverse medical complications and had longer hospital admissions compared to those not referred. Physicians were best able to predict adverse cardiac and diabetes-related complications. Half of the patients who were referred for postoperative assessment were lost to follow-up, and there was a trend towards increased hospital readmissions in this group. Further research is required to identify the subset of patients who might benefit from postoperative inpatient medical assessment.
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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 | 0.023 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".