Patient, provider, and system‐level factors associated with preoperative cardiac testing: A systematic review
Bibliographic record
Abstract
BACKGROUND: Overuse of preoperative cardiac testing contributes to high healthcare costs and delayed surgeries. A large body of research has evaluated factors associated with variation in preoperative cardiac testing. However, patient, provider, and system-level factors associated with variation in testing have not been systematically studied. OBJECTIVE: To conduct a systematic review to better delineate the patient, provider, and system-level factors associated with variation in preoperative cardiac testing. METHODS: We included studies of an adult US population evaluating a patient, provider, or system-level factor associated with variation in preoperative cardiac testing for noncardiac surgery since 2012. Our search strategy used terms related to preoperative testing, diagnostic cardiac tests, and care variation with Ovid MEDLINE and Embase from inception through January 2023. We extracted study characteristics and factors associated with variation and qualitatively analyzed them. We assessed risk of bias using the Newcastle-Ottawa Scale and Evidence Project Risk of Bias tool. RESULTS: Twenty-eight articles met inclusion criteria. Older age and higher comorbidity were strongly associated with higher-intensity testing. The evidence for provider and system-level covariates was weaker. However, there was strong evidence that a focus on primary care and away from preoperative clinic and cardiac consultations was associated with less testing and that interventions to reduce low-value testing can be successful. CONCLUSIONS: There is significant interprovider and interhospital variation in preoperative cardiac testing, the correlates of which are not well-defined. Further work should aim to better understand these factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".