Hospitalization Spending for Inpatients Affected by Sleep Disorders: Results from Hunan, China
Bibliographic record
Abstract
Abstract Background Sleep disorders are conditions that result in alters in normal sleeping. There are many sleep disorders and the most frequently discussed major sleep disorders include insomnia, in which the patients have difficulty falling asleep or staying asleep throughout the night; sleep apnea, in which the patients experience abnormal patterns in breathing while sleeping; and narcolepsy, a condition characterized by extreme sleepiness during the day and falling asleep suddenly during the day.Methods Population health data were extracted from Chinese National Health Statistics Network Reporting System (CNHSNRS) for the province of Hunan in China. Patients affected by sleep disorders (ICD-10 codes F51.0 and G47.0) as the principal diagnosis and hospitalized between 1 January 2017 and 31 December 2019 were included. Information on age, gender, number of comorbidities, type of sleep disorders, level of hospital, location of hospital, hospital costs, length of stay, admission year, and method of payment were retrieved from CNHSNRS. Median with interquartile range (IQR) was used to describe the hospital costs and Kruskal-Wallis test was conducted to examine significant differences among different groups. Quantile regression was applied to investigate how the hospital costs at low and high quantile of the distribution vary across groups.Results Hospital costs for patients suffering from sleep disorders were substantial: $1229.31 per patient, nearly a quarter and almost all per capita disposable yearly household income of urban ($5,024.30) and rural ($1,914.53) residents in Hunan in 2017, respectively. The study also identified several significant independent variables associated with hospital costs, including age, gender, type of sleep disorder, and hospital characteristics. In-hospital care for sleep apnea accounted for almost 80% of the total spending, and about half of the spending was from the patient’s pocket money.Conclusion Hospital costs for patients suffering from sleep disorders were substantial: nearly a quarter and almost all per capita disposable yearly household income of urban and rural residents in Hunan in 2017, respectively. Age, gender, type of sleep disorder, and hospital characteristics were significant independent variables associated with hospital costs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".