Hospitalization Rates Are Highest in the First 5 Years of Systemic Sclerosis: Results From a Population-based Cohort (1980–2016)
Bibliographic record
Abstract
Objective. Few studies have estimated the healthcare resource usage of patients with systemic sclerosis (SSc). The purpose of this study was to compare hospitalization among incident cases of SSc vs age- and sex-matched comparators. Methods. A retrospective, population-based cohort of patients with SSc in Olmsted County, Minnesota, from January 1, 1980, to December 31, 2016, was assembled. A 2:1 cohort of age- and sex-matched patients without SSc from the same population was randomly selected for comparison. All hospitalizations in the geographic area from January 1, 1987, to September 30, 2018, were obtained. Rates of hospitalization, lengths of stay, and readmissions were compared between groups. Results. There were 76 incident SSc cases and 155 non-SSc comparators (mean age 56 ± 16 yrs at diagnosis/index, 91% female) included. Rates of hospitalization among cases and comparators were 31.9 and 17.9 per 100 person-years, respectively (rate ratio [RR] 1.78, 95% CI 1.52–2.08). Hospitalization rates were higher in patients with SSc than comparators during the first 5 years after SSc diagnosis (RR 2.16, 95% CI 1.70–2.74). This difference decreased over time and was no longer significant at ≥ 15 years after SSc incidence/index. Lengths of stay (median [IQR] 4 [2–6] vs 3 [2–6], P = 0.52) and readmission rates (25% vs 23%, P = 0.51) were similar between groups. Conclusion. Patients with SSc were hospitalized more frequently than comparators, indicating high inpatient care needs in this population. Hospitalization rates were highest during the first 5 years following SSc diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".