Methods for Analyzing Hospital Length of Stay with Application to Inpatients Dying in Southern Thailand
Bibliographic record
Abstract
This study investigated length of stay (LOS) for patients who died in hospital in Southern Thailand from 2000 to 2003with respect to principal diagnosis and demographic, geographic and hospital size factors. The computerized data of40,498 mortality cases were obtained from the Ministry of Public Health from 167 hospitals in 14 provinces of SouthernThailand between October 2000 and September 2003 with information on age, gender, principal diagnosis, province andhospital size. Logistic and linear regression with log-transformed LOS was used to analyze the data. Patients withinjuries as principal diagnosis had shortest LOS, whereas cancer patients had the longest LOS. Older patients,particularly females, had higher LOS for all diagnoses. LOS increased with hospital size except in the North and NorthWest. Small hospitals in the South West region had the lowest LOS whereas large hospitals in the North West had thehighest. The highest proportion of bed days (11.2%) occurred in males aged less than 60 diagnosed with infectiousdiseases. Males aged less than 60 diagnosed with injuries and digestive diseases, and aged at least 60 diagnosed withCOPD, and aged less than 60 diagnosed with infectious diseases, accounted for more than double those for femalepatients in the same disease groups. Both logistic regressions with LOS at least 1 week as the outcome and linearregression on appropriately log transformed LOS gave consistent results. Providing suitable palliative care or allowingpatients to select the place for spending their final time of life, especially for patients with chronic diseases, which canreduce hospital resource utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".