Effect of insurance type on Management of Vancouver B Periprosthetic Fractures: length of stay, discharge destination and cost implications
Bibliographic record
Abstract
BACKGROUND: To assess the effect of private versus public insurance on hospital length of stay, discharge destination, and costs in managing Vancouver B periprosthetic femoral fractures (PFF). METHODS: A retrospective cohort study of PFF patients operatively managed at five public trauma centers. The primary outcome was hospital length of stay. Secondary outcomes included discharge destination and costs related to implants and hospital beds. RESULTS: The study included 195 PFF cases (133 public, 62 private). Private patients had lower ASA scores (2.8 versus 3.1, P = 0.006) and were more likely to come from independent residences (87% versus 74%, P = 0.045). Private patients spent 8 fewer days in the hospital (12 ± 8 versus 20 ± 19 days, P < 0.001) and were more often discharged to rehabilitation (74% versus 48%, P = 0.003). Public hospital costs were higher for public patients ($37 456 versus $25 324, P = 0.005), largely due to longer stays. Implant costs were similar between private and public patients, but patients that underwent revision surgeries increased costs significantly compared to patients that underwent open reduction and internal fixation alone ($6257 versus $3511, P < 0.001). CONCLUSION: Private insurance was linked to shorter hospital stays and increased discharge to rehabilitation. Public PPF patients incur an average cost of $37 456 for public hospitals, compared to $25 324 for private patients. Delays in public patient rehabilitation access may prolong hospital stays, suggesting a need for alternative care pathways, such as PPF tailored home-based rehabilitation and support programs.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".