Effect of US State Certificate of Need regulation of operating rooms on surgical resident training
Bibliographic record
Abstract
PURPOSE: Government regulation of health care services helps prevent costs associated with expansion and duplication of services in the United States. Certificate of Need (CON) helps restrict construction of ambulatory surgery facilities and hence controls delivery of surgical intervention, but concern exists about whether this affects resident exposure to an appropriate caseload. This study investigated how CON laws impact on surgical caseload as an index of resident surgical training. METHODS: This retrospective study used State Inpatient Data compiled by the Health Care Utilization Project. Mean per capita rates of 26 diverse surgical procedures were evaluated in 21 states with CON laws and 5 states without between 2004 and 2006. The proportion of procedures performed in teaching facilities was also assessed. Student's t-tests were used to evaluate differences in these parameters between regulated and non-regulated states (a = 0.05). Multivariate analysis of variance permitted evaluation of the types of procedures that underwent shift in location performed. RESULTS: States with CON laws did not differ significantly in procedural rates for any of the investigated surgical procedures; however, such regulation was associated with different trends in teaching center caseload, depending on the type of procedure. Complex procedures, such as Whipple operations (p = 0.14) or resection of acoustic neuroma (p = 0.37), underwent no redistribution. Conversely, common procedures that might have previously been performed in private settings, such as total hip replacement (p = 0.003) or mastectomy (p = 0.01), did occur more commonly in teaching facilities under CON regulation. CON law did not result in relocation of surgical procedures away from teaching institutions. CONCLUSIONS: These results suggest that government regulations do not discriminate against teaching facilities. Surgical residents in states with such regulation gain similar or superior exposure to procedures as residents in states without such laws.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".