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Record W4366084698 · doi:10.1136/rmdopen-2022-002808

Patient appropriateness for total knee arthroplasty and predicted probability of a good outcome

2023· article· en· W4366084698 on OpenAlex
Gillian Hawker, Éric Bohm, Michael Dunbar, Peter Faris, C Allyson Jones, Tom Noseworthy, Bheeshma Ravi, Linda J. Woodhouse, Deborah A. Marshall

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRMD Open · 2023
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of AlbertaUniversity of CalgaryUniversity of TorontoDalhousie UniversityUniversity of ManitobaWomen's College Hospital
FundersUniversity of TorontoCanadian Institutes of Health ResearchDalhousie UniversityUniversity of Alberta
KeywordsMedicineTotal knee arthroplastyOutcome (game theory)Physical therapyArthroplastySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: One-fifth of total knee arthroplasty (TKA) recipients experience a suboptimal outcome. Incorporation of patients' preferences in TKA assessment may improve outcomes. We determined the discriminant ability of preoperative measures of TKA need, readiness/willingness and expectations for a good TKA outcome. METHODS: In patients with knee osteoarthritis (OA) undergoing primary TKA, we preoperatively assessed TKA need (Western Ontario-McMaster Universities OA Index (WOMAC) Pain Score and Knee injury and Osteoarthritis Outcome Score (KOOS) function, arthritis coping), health status, readiness (Patient Acceptable Symptom State, depressive symptoms), willingness (definitely yes-yes/no) and expectations (outcomes deemed 'very important'). A good outcome was defined as symptom improvement (met Outcome Measures in Rheumatology and Osteoarthritis Research Society International (OMERACT-OARSI) responder criteria) and satisfaction with results 1 year post TKA. Using logistic regression, we assessed independent outcome predictors, model discrimination (area under the receiver operating characteristic curve, AUC) and the predicted probability of a good outcome for different need, readiness/willingness and expectations scenarios. RESULTS: Of 1,053 TKA recipients (mean age 66.9 years (SD 8.8); 58.6% women), 78.1% achieved a good outcome. With TKA need alone (WOMAC pain subscale, KOOS physical function short-form), model discrimination was good (AUC 0.67, 95% CI 0.63 to 0.71). Inclusion of readiness/willingness, depressive symptoms and expectations regarding kneeling, stair climbing, well-being and performing recreational activities improved discrimination (p=0.01; optimism corrected AUC 0.70, 0.66-0.74). The predicted probability of a good outcome ranged from 44.4% (33.9-55.5) to 92.4% (88.4-95.1) depending on level of TKA need, readiness/willingness, depressive symptoms and surgical expectations. CONCLUSIONS: Although external validation is required, our findings suggest that incorporation of patients' TKA readiness, willingness and expectations in TKA decision-making may improve the proportion of recipients that experience a good outcome.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.299
Teacher spread0.262 · 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