DISEASE, PATIENT AND HEALTHCARE SYSTEM LEVEL PREDICTORS OF ACCUMULATED INPATIENT DAYS AND HOME CARE USE FOR METASTATIC GASTRIC CANCER PATIENTS
Bibliographic record
Abstract
Introduction Home care has been proposed as a means of reducing costs in palliative care by decreasing inpatient stay without impacting quality of clinical care. Predictors of major cost drivers for end-of-life care are unknown for the management of metastatic gastric cancer. Objectives This study examined disease, patient and healthcare system predictors of inpatient hospital days and receipt of home care. Methods This is a population-based, retrospective cohort study of data on patients diagnosed in Ontario between 2005 and 2008. Chart review and administrative data were linked, using a twenty-six month time horizon and the healthcare system perspective. The cumulative inpatient hospital stay was defined using admission and discharge dates from hospitalizations in the Canadian Institute for Health Information database. Home care use was defined as yes/no, using data from the Ontario Home Care Database. Negative binomial regression was used to model the number of inpatient hospital days and modified poisson regression to model the receipt of home care. Results Patients with primary tumours in the gastroesophageal junction compared to the distal stomach, and younger age incurred significantly fewer inpatient days. Patients who underwent a gastrectomy were significantly less likely to accumulate inpatient hospital days (RR 0.65; 95% CI 0.55-0.76), as were patients who interacted with a high volume specialist (RR=0.54; 95% CI=0.46-0.63). Proximal compared to distal tumour location was associated with an increased likelihood of receiving homecare (RR=1.12; 95% CI: 1.04-1.20). Increasing age was significantly associated with not receiving a home care visit (p=0.0010). Patients in the high resource use category were 78% more likely to receive home care than healthy users (RR=1.78; 95% CI=1.02-3.08). Patients interacting with a high volume specialist were 15% more likely to receive home care than those who did not (RR=1.15; 95% CI=1.09-1.21). Conclusion A number of predictors of healthcare resource utilization were identified; however, not all were modifiable. Further research needs to examine how differences in home care use and inpatient hospital stay impact clinical outcomes such as symptom relief and quality of life, and how policies may be targeted to reduce costs to the healthcare system while maintaining optimal clinical care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".