Utility of Electrocardiogram and Chest X-Ray for Preoperative Evaluation in Benign Hysterectomy
Bibliographic record
Abstract
Abstract Objective: The aims of this research were to evaluate concordance of practice patterns for preoperative electrocardiogram (EKG) and chest X-ray for benign hysterectomy with institutional and international guidelines, and to assess implications for perioperative management. Materials and Methods: For this retrospective cohort study (Canadian Task Force II-2), medical records of 587 women who underwent hysterectomy for benign indications at a single institution, from January 1 through December 31, 2016, were reviewed. Each EKG and chest X-ray (CXR) was evaluated for indications, concordance with institutional and with the United Kingdom's National Institute for Health and Care Excellence (NICE) guidelines, results, effects on management, and association with perioperative complications. Descriptive statistics were used for analysis. Results: Subjects were, on average, 45 (range: 27–76) years old, with primarily Hispanic (58.4%) and black (30.0%) race. For 587 hysterectomies, 182 EKGs (31.0%) and 70 CXRs (11.9%) were ordered. EKG was indicated for s90 (49.5%) patients per institutional criteria and for 177 (97.3%) patients per NICE criteria. Findings were abnormal in 81 (44.5%) of 182 EKGs. Further work-up was pursued for 16 (19.8%) patients. There were 2 case delays, but no cancellations. Notable perioperative events included nonspecific EKG changes and airway edema in 2 patients with abnormal preoperative EKGs. Of 70 CXRs ordered, 17 (24.3%) were in concordance with institutional criteria. No CXRs were indicated per NICE criteria. There were abnormal findings on 10 (14.3%) chest X-rays. The anesthesia department was consulted for 1 patient. There were no case delays/cancellations or perioperative cardiopulmonary complications. Conclusions: Preoperative EKGs and CXRs offer little clinical utility for patients undergoing benign hysterectomy.
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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 | 0.004 | 0.003 |
| 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 | 0.000 |
| 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".