Population-based analysis of hospital readmissions within 1 year of esophagectomy.
Bibliographic record
Abstract
e15580 Background: Hospital readmission after esophagectomy is a useful metric for measuring resource utilization. Our objective was to evaluate readmission rates within 1 year of discharge home after esophagectomy. Methods: We conducted a population-based retrospective cohort study using linked health administrative data on all patients who had an esophagectomy for cancer between 2000 and 2012 in Ontario, Canada. Ontario utlizes a single-payer healthcare system with a population of 13.8 million people. Univariable and multivariable analyses were used to identify factors associated with readmission within 1 year of discharge. Results: We identified 3344 patients who had an esophagectomy. In-hospital mortality was 5.8% (n = 193). Readmission within 1 year occurred in 1651 patients ( 49.4%) and 803 patients (24%) died within 1 year following esophagectomy. On multivariable analysis, higher comorbidity status (adjusted odds ratio [aOR] = 1.04, 95%CI 1.01-1.06), use of chemotherapy (aOR = 1.46, 95%CI 1.22-1.75) or radiation therapy (aOR = 1.63, 95%CI 1.33-2.01) were significantly associated with higher rates of readmission. Age, sex, income and rural status were not significantly associated with higher rates of readmission (p > 0.20).There were significantly lower rates of readmission over the 2000-2012 study period (aOR = 0.95, 95%CI 0.93-0.97). There was a consistent annual reduction in readmission rates with 52.8% readmission in 2000 versus 43.8% in 2012 (p < 0.0001). The point at which readmission rates went below 50% occurred between 2006 & 2008. Conclusions: Readmission after surgery is a potential quality indicator but may also be related to underlying disease, in this case cancer of the esophagus or gastroesophageal junction. We found a high (49.4%) rate of hospital readmission within 1 year of discharge following esophagectomy. Higher comorbidity status & use of chemo/radiation therapy were independently associated with higher rates of readmission whereas age or socioeconomic or rural status were not. There was a consistent & significant trend of reduced readmission rates over the study period. Regionalization of esophagectomy occurred over this time period and may have contributed to this trend.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".