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Record W2808863897 · doi:10.1186/s12955-018-0955-2

The prognostic value of pain catastrophizing in health-related quality of life judgments after Total knee arthroplasty

2018· article· en· W2808863897 on OpenAlexafffundabout
Esther Yakobov, William D. Stanish, Michael Tänzer, Michael Dunbar, Glen Richardson, Michael Sullivan

Bibliographic record

VenueHealth and Quality of Life Outcomes · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsDalhousie UniversityMcGill University
FundersInstitute of Musculoskeletal Health and ArthritisCanada Research Chairs
KeywordsQuality of life (healthcare)Total knee arthroplastyMedicinePhysical therapyPain catastrophizingArthroplastyValue (mathematics)Health related quality of lifePhysical medicine and rehabilitationSurgeryChronic painInternal medicineNursingDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Total knee arthroplasty (TKA) is a highly effective procedure that yields reductions in pain and disability associated with end stage osteoarthritis (OA) of the knee. Quality of life instruments are frequently used to gauge the outcomes of total knee arthroplasty (TKA). However, research suggests that post-TKA reductions in symptom severity may not be the sole predictors of quality of life post-TKA. The primary objective of the present study was to examine the prognostic value of catastrophic thinking in health-related quality of life (HRQoL) judgments in patients with severe OA after TKA. METHODS: In this study we used a prospective cohort design to examine the value of pain catastrophizing in predicting HRQoL 1 year after TKA. Participants with advanced OA of the knee who were scheduled for TKA were recruited at one of three hospitals in Canada. The study sample consisted of 116 individuals (71 women, 45 men) who completed study questionnaires at their pre-surgical evaluation and 1 year after surgery. Hierarchical regression analysis was used to assess the unique contribution of pre-surgical pain catastrophizing to the prediction of post-surgical HRQoL judgments. RESULTS: The results of the hierarchical regression equation revealed that the overall model was significant, F (9,106) = 8.3, p < 001, and accounted for 36.4% of the variance in the prediction of post-surgical physical component score of HRQoL. Pain catastrophizing was entered in the last step of the equation and contributed significant unique variance (β = -.35, p < .001) to the prediction of post-surgical physical component score of HRQoL above and beyond the variance accounted for by demographic variables, co-morbid health conditions, baseline HRQoL, and post-surgical reductions in pain, joint stiffness and physical disability. CONCLUSIONS: The current findings highlight the importance of pre-surgical catastrophic cognitions in influencing HRQoL judgments after TKA. The findings suggest that psychosocial interventions designed to reduce pain catastrophizing before TKA might contribute to better quality of life outcomes following surgery.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.050
GPT teacher head0.344
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2018
Admission routes3
Has abstractyes

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