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Record W4405838122 · doi:10.2106/jbjs.24.00685

Shared Decision-Making in Total Hip and Knee Arthroplasty

2024· article· en· W4405838122 on OpenAlexaff
Elizabeth A. Kroll, Robert E. Schlegel, Charles M. Evarts, Patricia D. Franklin, Conrad Persels, Nancy A. Mullen, Mary Beth Crummer, Sally P. Seeley, Sue Lockett, Wayne E. Moschetti, James Nace, Eric M. Cohen, Brent A. Lanting, Richard Iorio, Antonia F. Chen, James A. Browne, Brock A. Lindsey, Michael S. Kain, Yale A. Fillingham, Richard M. Terek, Kevin L. Garvin, James I. Huddleston, Stephanie F. Chomos, Kimberly M. Lewis, Carol A. Lambourne, Vincent D. Pellegrini

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineContext (archaeology)Physical therapyArthroplastyQuality of life (healthcare)Joint replacementDelphi methodKnee replacementSurgeryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Although total hip and total knee arthroplasty are highly successful operations, the decision of whether and when to undergo surgery is highly subjective and discretionary, and specific guidelines regarding readiness for surgery remain elusive. The nature of these decisions underscores the importance of shared decision-making, which is founded on the concept that patients substantially contribute to determining their own readiness for surgery. The OPTION survey was developed as a conversation aid to facilitate shared decision-making in the context of total joint arthroplasty. METHODS: The OPTION survey was created in partnership with a panel of 10 active joint replacement patients and 15 arthroplasty surgeons, using a modified Delphi methodology that employed 3 sequential meetings by each group. The survey interrogates patient and surgeon ratings of pain, activity limitation, duration of treatment, prior treatments, and quality of life; patient-rated treatment priorities, readiness for surgery, and surgeon engagement; and surgeon-graded radiographic disease. The survey was administered as an institutional review board-approved pilot during 641 patient-clinician encounters for hip or knee arthritis at 9 U.S. sites, and was independently completed by the patient and surgeon. RESULTS: Patient self-assessment of readiness for surgery includes consideration of existing functional impairment, outcome priorities, realistic expectations, and personal socioeconomic circumstances. Patients most commonly ranked removal of activity limitations as their top treatment priority, while alleviation of pain and avoidance of a long recovery were also ranked highly. Mild and severe pain were associated with similar radiographic disease severity, and worsening radiographic disease was associated with increasing patient-reported readiness for surgery. Patients and surgeons agreed on symptom severity in >90% of cases. When disagreement occurred, surgeons typically underestimated patient-reported symptoms; these cases were associated with lower patient-rated surgeon engagement in shared decision-making conversations. CONCLUSIONS: Shared decision-making conversations substantially contributed to the assessment of patient readiness for joint replacement surgery. When patient and surgeon assessments were not aligned, surgeons most commonly underestimated patient-perceived impairment. These observations should inform optimal surgeon-patient communications. LEVEL OF EVIDENCE: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

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)0.083
metaresearch head score (Gemma)0.136
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.136
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.008
Scholarly communication0.0060.004
Open science0.0020.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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