PAPER 116: PREDICTORS OF ACUTE HOSPITAL LENGTH OF STAY FOLLOWING A HIP OR KNEE REPLACEMENT
Bibliographic record
Abstract
Purpose: Elective total hip and knee replacement surgeries are effective procedures for patients suffering from hip and knee disease. The demand for joint replacements is expected to rise as the life expectancy of Canadians increases; thus putting a heavy burden on healthcare. In an effort to reduce the acute hospital length of stay (LOS) the Alberta Orthopaedic Society, with the Alberta Bone and Joint Health Institute, three Alberta health regions (Calgary, Capital and David Thompson) and Alberta Health and Wellness created an evidence based new care continuum for hip and knee replacement. The LOS through the new care continuum compared to the current conventional approach was evaluated. In addition patient characteristics that could potentially predict the LOS were evaluated. Method: The study design was a randomized, controlled trial. Consenting subjects were randomized to receive care through either the new care continuum (intervention) or the existing “current conventional approach” (control). Acute hospital LOS was calculated as the difference between the date and time the patient was admitted to the date and time the patient was discharged. Data was collected on patient characteristics potentially associated with acute hospital LOS. Results: Intervention patients demonstrated a significantly shorter acute hospital LOS than the control patients, 4.66 and 5.95 days respectively. Further analysis of the data using a generalized linear model indicated that several patient characteristics were associated with a shorter/longer wait for consultation and surgery. Married patients had a statistically significant shorter LOS than single patients (IRR=0.89, p=0.001). Whereas older patients (IRR=1.01, p= Conclusion: This study indicated that an evidence based healthcare continuum for the delivery of hip and knee replacements was successful in significantly reducing acute care LOS. Reducing the LOS using the new care continuum could potentially help alleviate the strain on limited healthcare resources and the savings could be reinvested to increase the numbers of joint replacement performed. Furthermore, an understanding of patient characteristics that influence acute hospital care LOS should be used to model surgical case mixing to further improve efficiencies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".