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Record W4415430176 · doi:10.1302/1358-992x.2025.10.059

WHEN DOES PATIENT FUNCTION “PLATEAU” AFTER TOTAL JOINT REPLACEMENT? A COHORT STUDY

2025· article· en· W4415430176 on OpenAlexaffabout
Seper Ekhtiari, Tina Worthy, L. Puri, Danielle Petruccelli, Justin de Beer, Trevor Wood

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsJoint arthroplastyArthroplastyConfoundingCohort studyRetrospective cohort studyCohortProspective cohort studyPatient-reported outcome

Abstract

fetched live from OpenAlex

With over 100,000 procedures done per year, hip and knee replacements are two of the most common surgical procedures performed in Canada. Post-operatively, Patient Reported Outcome Measures (PROMs) will start to plateau with time as patients reach the limits of how a prosthetic joint can function for them. There has been literature indicating that PROMs will start to plateau between 6 and 12 months. Having an understanding of the precise timeframe, as well as the factors that can impact this trajectory, can be beneficial for setting patient expectations, and directing patient follow-up. Thus, our purpose was to analyze the trajectory of PROMs following total hip and knee arthroplasty (THA and TKA), as well as assess the impact of any potential confounders on this trajectory. This study was a retrospective analysis of data from a prospective database of a consecutive series of primary THA and TKA patients. The database is collected at a single academic arthroplasty centre, located in Ontario, Canada. Patients were eligible if they had undergone an elective, primary THA/TKA with Oxford Scores recorded pre-operatively, and at least at two of the following three time points: 6 weeks, 6 months, 1 year, and 2 years. Clinically meaningful change from one timepoint to the next was defined as 5 points, based on previously established minimal clinically important difference (MCID) values for both hip and knee scores. General linear models with repeated measures were used to assess for change between timepoints. American Society of Anesthesiologist (ASA) class and date of operation were included as potential between subject factors in the respective hip and knee models. Mean data reported along with standard deviation in parentheses. Overall, 8,276 joints were eligible for inclusion in the study. There were more female patients, with 60.4% being female (N = 5,000). Mean age was 67.9 years (9.6), while mean body mass index (BMI) was 31.6 kg/m2 (6.8). Nearly two-thirds (66.1%) of operative joints were knees (5,467/8,276). Mean pre-operative scores were 18.0 (7.8) for THA, and 20.1 (7.5) for TKA. For both THA and TKA, there were statistically significant interval improvements in Oxford scores from 6 weeks [THA: 33.8 (7.9)/TKA: 28.7 (7.8)] to 6 months [THA: 40.2 (7.3)/TKA: 35.9 (8.3)], and from 6 months to 1 year [THA: 41.0 (7.3)/TKA: 37.3 (8.4)], but not from 1 to 2 years [THA: 40.0 (8.5)/TKA: 36.4 (9.6)]. The change from 6 months to 1 year for both hip and knee patients was below the threshold for a clinically meaningful change based on an MCID value of 5.0. None of the between-subject factors identified as potential sources and bias and included in the model (BMI, ASA, or date of surgery) demonstrated a significant interaction effect. Patients undergoing both THA and TKA can expect clinically meaningful improvements up to 6 months, at which point there is a plateau in PROM scores. These findings are important for both for setting patient expectations in pre-operative discussions, and allowing surgeons to have a realistic understanding of their patients' expected post-operative course.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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