Demographic and Psychosocial Predictors of Acute Perioperative Pain for Total Knee Arthroplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: As the North American population ages, the prevalence of knee osteoarthritis and the surgical interventions (ie, total knee arthroplasty [TKA]) aimed at correcting pain and disability will also rise proportionally. Therefore, efforts to better understand the factors associated with surgical outcomes are warranted. To date, no studies have examined the impact of psychosocial factors on acute postoperative TKA pain. OBJECTIVES: The primary objective was to examine the associations among catastrophizing, negative mood, demographics and acute postoperative pain following TKA. Ancillary analyses examined the association of preoperative psychological variables with postoperative pain. METHODS: Patients completed questionnaire packages 2 h before their surgery and on three consecutive postoperative days while in the hospital. The questionnaire packages included the Short Form - McGill Pain Questionnaire, the Pain Catastrophizing Scale and the Shortened Version of Profile of Mood States. The Mini-Mental State Examination was also administered. Demographic data were extracted from patients' medical charts. RESULTS: Associations among catastrophizing, negative mood and pain were established. Regressions showed that younger age predicted greater preoperative and postoperative day 1 pain; catastrophizing predicted preoperative and postoperative day 2 pain; and negative mood predicted postoperative day 3 pain. Catastrophizing and negative mood were highly correlated at several assessment points. Preoperative variables did not predict postoperative pain. CONCLUSION: These results have postoperative pain management implications. Heightened attention to psychosocial variables, such as postoperative catastrophizing and negative mood, may be useful in identifying patients at risk for greater postoperative pain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it