Discharge Disposition After Limb Amputation in a Publicly Funded Health Care System
Bibliographic record
Abstract
Understanding discharge disposition (DD) after limb amputation (LA) surgery allows health care providers and policy makers to adapt resources based on need. Studying independent prognostic factors for DD after LA in Canada eliminates the significant influence of payor source, as reported by researchers in the United States. We hypothesize disparities exist among DDs after LA in a publicly funded health care system. Retrospective review of Saskatchewan's linked administrative health data from 2006 to 2019 was used to identify independent socio-demographic factors, amputation levels, amputation predisposing factors (APF), and surgical specialty on 5 DD's: inpatient, continuing care, home with support services (H/W), home with no support services (H/WO), and those who died at the hospital after LA. We found age, amputation level, and APF play a significant role in determining discharge to all dispositions; gender was significantly associated with discharge to continuing care and H/WO; place of residence was associated with discharge to inpatient facilities, continuing care, and H/W; income was not associated with any DD other than H/W; surgical specialty was associated with discharge to all dispositions except death. The findings suggest that disparities in DD following LA exist even after eliminating the influence of payor source. Health care providers and policy makers should consider these findings in preparation for future needs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".