Abstract 18075: Resource Utilization in Patients with Mitral Regurgitation Before and After Surgical Intervention: An Analysis of the Centers for Medicare and Medicaid Services Database
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to characterize resource utilization in Medicare beneficiaries with mitral regurgitation by comparing hospitalization rates and costs before and after surgical intervention. METHODS: De-identified inpatient claims data were obtained from the Centers for Medicare and Medicaid services for 1/1/2001 to 12/31/2005. Medicare beneficiaries with functional mitral regurgitation (ICD9: 424.0) who received mitral valve surgery (ICD9 codes: 35.12, 35.23, 35.24, 35.33) during this time period were included in analysis (N=68,344). Only claims data 1 year before (N=44,190) and 1 year after surgical intervention (N=78,600) was analyzed. Costs from surgical intervention hospitalization was excluded from analysis. Outcome measures included median inpatient costs, hospitalization rates and length of stay. Costs were estimated using ratio-of-costs-to-charges, and age-adjusted hospital costs were compared using quantile regression. Primary ICD9 diagnosis code was used to categorize type of hospitalization. RESULTS (TABLE): Median hospitalization costs for patients prior to surgical intervention were higher than after (p<0.05). Mean hospitalization rate in the year prior to surgical intervention was lower than in the year immediately following (p<0.001). Mean LOS was also longer after surgical intervention compared to before (p<0.001). A greater proportion of hospitalizations before surgery were associated with Congestive Heart Failure (ICD9: 428.0) compared to after (p<0.001). CONCLUSIONS: Even though LOS and readmission rates are higher in Medicare beneficiaries in the year following surgical intervention, hospitalization costs are significantly lower. This may be secondary to fewer Congestive Heart Failure hospitalizations after surgical intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".