The impact of frailty on outcomes and healthcare resource usage after total joint arthroplasty
Bibliographic record
Abstract
AIMS: Total joint arthroplasty (TJA) is commonly performed in elderly patients. Frailty, an aggregate expression of vulnerability, becomes increasingly common with advanced age, and independently predicts adverse outcomes and the use of resources after a variety of non-cardiac surgical procedures. Our aim was to assess the impact of frailty on outcomes after TJA. PATIENTS AND METHODS: We analysed the impact of pre-operative frailty on death and the use of resources after elective TJA in a population-based cohort study using linked administrative data from Ontario, Canada. RESULTS: Of 125 163 patients aged > 65 years having elective TJA, 3023 (2.4%) were frail according to the Johns Hopkins ACG frailty-defining diagnoses indicator. One year follow-up was complete for all patients. Frail patients had a higher adjusted one year risk of mortality (hazard ratio 3.03, 95% confidence interval (CI) 2.62 to 3.51), a higher rate of admission to intensive care (odds ratio (OR) 2.52, 95% CI 2.21 to 2.89), increased length of stay (incidence rate ratio 1.62, 95% CI 1.59 to 1.65), a higher rate of discharge to institutional care (OR 2.09, 95% CI 1.93 to 2.25), a higher rate of re-admission (OR 1.33, 95% CI 1.07 to 1.66) and increased costs at 30, 90 and 365 days post-operatively. Frailty affected outcomes after total hip arthroplasty more than after total knee arthroplasty. TAKE HOME MESSAGE: Frailty is an important risk factor for death after elective TJA, and increases post-operative resource utilisation across many metrics. Processes to optimise the outcomes and efficiency of TJA in frail patients are needed. Cite this article: Bone Joint J 2016;98-B:799-805.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".