Impact of preoperative factors on clinical outcomes after total hip arthroplasty
Bibliographic record
Abstract
BACKGROUND: Although total hip arthroplasty (THA) is an established intervention for advanced hip disorders, not all patients achieve the anticipated functional improvements. AIM: To investigate the impact of various preoperative factors on clinical outcomes after THA. METHODS: Data of 411 patients who underwent unilateral THA were retrospectively analyzed. The associations between preoperative factors, such as age, body mass index, pain severity, functional impairment, psychological status, neuropathic pain, and central sensitization, and clinical outcomes assessed six months postoperatively using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and modified Harris Hip Score were evaluated. RESULTS: Our results indicated that age and the WOMAC, Center for Epidemiologic Studies Depression Scale, and Central Sensitization Index (CSI) scores significantly predicted the modified Harris Hip Score outcomes, whereas age and preoperative WOMAC, EuroQol 5 dimensions, Center for Epidemiologic Studies Depression Scale, CSI, and Pain Detect Questionnaire scores were significant predictors of WOMAC outcomes. Age, WOMAC, and CSI were consistently significant factors. There were no significant differences in the operative time or blood loss across the outcome categories. CONCLUSION: Our findings highlight the importance of preoperative assessment of central sensitization and psychological parameters. Patient-specific preoperative characteristics may play a greater role than intraoperative factors in determining recovery outcomes after THA.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".