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Record W4415929182 · doi:10.2106/jbjs.25.01006

Weight Loss After Joint Replacement: Fact or Fiction?

2025· article· en· W4415929182 on OpenAlexaff
Lisa C. Howard

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPeriprostheticOverweightBody mass indexArthroplastyJoint arthroplastyWeight lossProsthesisAmputation

Abstract

fetched live from OpenAlex

Commentary As the debate continues among surgeons regarding the importance of body mass index (BMI) in patients undergoing primary total hip arthroplasty (THA) or total knee arthroplasty (TKA), the article by Karczewski et al. is well-timed. A staggering one-half of patients undergoing primary TKA and one-third of patients undergoing THA1,2 have a BMI of ≥30 kg/m2. It is known that obesity increases surgical risks, including instability, thromboembolic events, and periprosthetic joint infection3,4, although the periprosthetic joint infection is often debated. Overweight patients often view joint replacement as a means to facilitate weight loss. However, weight loss is a multifactorial process, with increased physical activity representing only 1 of several contributing factors. Performing a joint replacement in a patient with high BMI can pose considerable technical surgical challenges. Achieving enough exposure for an adequate debridement is challenging on its own, and the excess adipose tissue often necroses, making a watertight closure difficult. In the most drastic of scenarios, if an excision arthroplasty or amputation is required, these patients are often not candidates for a prosthesis; the stakes are higher. In their retrospective study over 11 years, Karczewski et al. included 763 patients who underwent primary THA or TKA and had recorded BMI measurements at the time of surgery as well as at follow-up points of 2, 5, and 10 years. They also included additional cohorts of patients who had BMI measurements recorded at surgery and at either 2, 5, or 10 years postoperatively. Although the mean BMI changes were significant at 2 and 5 years for both patients who underwent THA and those who underwent TKA, these changes were small. The authors concluded that, overall, there was no meaningful change in mean BMI, defined in accordance with the U.S. Food and Drug Administration as >5% total body weight loss. However, the distribution of the change tells a more interesting story. At 10 years, a >5% change in BMI after THA occurred in 57% of patients, with 30% increasing and 27% decreasing by >5% relative to their BMI at surgery. The TKA group was similar, with 32% increasing and 30% decreasing in BMI by >5%. Also, although almost one-third of patients did lose meaningful weight at 10 years, another almost one-third of patients gained meaningful weight. Female patients were found to have twice the risk of gaining >5% of their BMI at 10 years after THA (odds ratio, 2.14; p = 0.006), whereas older patients were less likely to gain at 10 years after TKA (odds ratio, 0.95; p < 0.001). Retrospective studies such as this will always face criticism, given the inherent bias in their design. Furthermore, the impact of BMI as it relates to joint replacement is heterogenous and a source of great debate. The authors should be commended for their large numbers, long study duration, and robust methodological attempts (well-executed analysis of variance and multinomial logistical regression) to circumvent the limitations of retrospective studies. However, the lack of generalizability outside of a predominantly White population at a single institution is an important limitation. Nevertheless, this study will hopefully jumpstart needed studies on other demographic and racial distributions. In addition, patients lost to follow-up, who may be systematically different, were excluded from this study. Although the BMI did not differ at surgery between the included patients and patients lost to follow-up, the impact of these missing data cannot be concretely determined. Finally, this article further illuminates the utility of BMI as a tool for weight assessment. Despite its ease of use, BMI does not take into account weight distribution, body composition, and other metabolic risk factors. Despite these limitations, Karczewski et al. produced meaningful research that contributes to and improves upon the current literature, as previous studies examining BMI changes after arthroplasty have been limited to a single short-term postoperative measurement5–8. The authors have given us evidence-based research to inform patients that joint replacement is not a solitary means for weight loss and does not replace adequate workup and management of risk factors that contribute to their overall risk. Put plainly, patients angling for a joint replacement as a motivation for weight loss are likely misinformed and require their expectations to be adjusted. The weight loss journey involves optimization of factors such as metabolic syndrome, insulin resistance, and diet, all of which are arguably more impactful than exercise alone. This study also suggests that, after arthroplasty, women gain weight more than men as they age, which should be factored into individual patient expectation discussions. Physicians should carefully consider the results of this study as a springboard for further research as well as to inform their practice, particularly as it relates to adjusting patient expectations.

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)0.006
metaresearch head score (Gemma)0.051
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0020.005
Scholarly communication0.0020.006
Open science0.0050.001
Research integrity0.0180.027
Insufficient payload (model declined to judge)0.0060.004

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 head0.259
Teacher spread0.240 · 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 routes1
Has abstractyes

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