Predicting early clinical function after hip or knee arthroplasty
Bibliographic record
Abstract
OBJECTIVES: Patient function after arthroplasty should ideally quickly improve. It is not known which peri-operative function assessments predict length of stay (LOS) and short-term functional recovery. The objective of this study was to identify peri-operative functions assessments predictive of hospital LOS and short-term function after hospital discharge in hip or knee arthroplasty patients. METHODS: In total, 108 patients were assessed peri-operatively with the timed-up-and-go (TUG), Iowa level of assistance scale, post-operative quality of recovery scale, readiness for hospital discharge scale, and the Western Ontario and McMaster Osteoarthritis Index (WOMAC). The older Americans resources and services activities of daily living (ADL) questionnaire (OARS) was used to assess function two weeks after discharge. RESULTS: Following multiple regressions, the pre- and post-operative day two TUG was significantly associated with LOS and OARS score, while the pre-operative WOMAC function subscale was associated with the OARS score. Pre-operatively, a cut-off TUG time of 11.7 seconds for LOS and 10.3 seconds for short-term recovery yielded the highest sensitivity and specificity, while a cut-off WOMAC function score of 48.5/100 yielded the highest sensitivity and specificity. Post-operatively, a cut-off day two TUG time of 31.5 seconds for LOS and 30.9 seconds for short-term function yielded the highest sensitivity and specificity. CONCLUSIONS: The pre- and post-operative day two TUG can indicate hospital LOS and short-term functional capacities, while the pre-operative WOMAC function subscale can indicate short-term functional capacities. Cite this article: Bone Joint Res 2015;4:145-151.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".