The Pain Disability Questionnaire (PDQ): evaluating the efficacy of the psychosocial and functional subscales for 12-month post-treatment outcomes after total knee arthroplasty
Bibliographic record
Abstract
Background: The Patient Disability Questionnaire (PDQ) has been shown to have good predictive validity for outcomes after total hip arthroplasty (TKA). However, the PDQ subscales (psychosocial and functional disability) have not been assessed independently. This study assesses 1-year surgical outcomes based on the PDQ’s psychosocial and functional disability subscale scores. Methods: The sample included 130 participants undergoing TKA. Physical and psychosocial measures included the PDQ, Knee Society Score (KSS), Western Ontario McMaster Arthritic Index (WOMAC), and the Short Form-36 Health Inventory (SF-36). Univariate correlations were used to cross-validate all subscales. Multivariate gamma and linear regressions were used to associate PDQ functional and psychosocial disability subscale scores with WOMAC and SF-36, 1 yr after TKA. Results: Poorer PDQ psychosocial scores were associated with being younger, black or Hispanic, and using Medicaid. Poorer functional disability scores were associated with being younger, female, black or Hispanic, and using Medicaid or without insurance. Multivariate regressions revealed that baseline PDQ psychosocial and functional disability were both significantly associated with 1-year WOMAC total scores and 1-year SF-36 Mental Composite Scores. Conclusions: The PDQ is useful in predicting 1-year outcomes for patients undergoing TKA. Worse baseline PDQ subscale scores indicated worse quality of life, more pain and stiffness of the joints, as determined by comparative WOMAC and SF-36 scores. Clinical implications from this study suggest screening patients before surgery to identify factors that could hinder recovery time or cause pain or function remission in adults undergoing TKA.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".