Not So Sweet
Bibliographic record
Abstract
Clearly identifying patients with prediabetes and diabetes prior to surgery allows the clinical team to target interventions and reduce the risks of complications. Yet, protocols for preoperative diabetes screening vary. The purpose of this article is to present an evidence-based practice project examining the implementation of preoperative diabetes screening in an elective total joint patient population. The American Diabetes Association (ADA) Risk Test was used to assess diabetes risk and guide further testing. A total of 121 patients were screened. Of the sample, 55 were undiagnosed and at risk for diabetes according to the instrument. Twelve patients (21.8%) who screened at risk also revealed elevated fasting blood glucose levels. These patients were identified as potentially having prediabetes. The findings of this project support adoption of the ADA Risk Test in the preoperative setting, emphasize the feasibility of its integration in order to obtain valuable patient information, and assist with optimizing patients for surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".