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Record W2038792399 · doi:10.1155/2013/517803

Comment on “The Effects of Bariatric Surgery Weight Loss on Knee Pain in Patients with Osteoarthritis of the Knee”

2013· article· en· W2038792399 on OpenAlexaboutno aff
Janice Lin, Manish Parikh, Jonathan Samuels

Bibliographic record

VenueArthritis · 2013
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisWeight lossWOMACObesitySurgeryTramadolGastric bypassPhysical therapyAnalgesicInternal medicineAnesthesiaAlternative medicine

Abstract

fetched live from OpenAlex

It was with great interest that we read “The Effects of Bariatric Surgery Weight Loss on Knee Pain in Patients with Osteoarthritis of the Knee” by Edwards et al. [1]. As other studies have shown, obesity is an incrementally modifiable risk factor for the development and progression of knee osteoarthritis (KOA) [2, 3]. Any opportunity to treat obesity and potentially limit KOA progression and disability will be an important public health strategy. Bariatric surgery is superior to regimented dietary and exercise programs in helping obese patients achieve and maintain weight loss [4, 5], and thus we think the authors focused on an important potential option to help obese patients avoid total joint replacement surgery. We found it valuable that the authors conducted a joint-driven and hypothesis-driven study to track pain and functional improvement in patients who underwent the three types of bariatric surgery: gastric bypass, sleeve gastrectomy, and laparoscopic adjustable gastric banding (LAGB). We agree with the authors that the existing literature is limited in examining the effect of bariatric surgery on KOA, particularly with confirmation of patients' radiographic KOA and the utilization of validated tools such as Western Ontario and McMaster Universities (WOMAC) Index of Osteoarthritis, Knee Osteoarthritis Outcome Score (KOOS). The authors provided encouraging data, but it was unclear whether medications including acetaminophen, nonsteroidal anti-inflammatory drugs, tramadol, narcotics, topical agents, or intra-articular injections were given to patients for their knee pain during the study period. Any change in a patient's treatment regimen for KOA may contribute to the improvement of pain and function, beyond weight loss alone from bariatric surgery, and impact the findings. While it is unreasonable to ask patients to avoid any pain medication during such studies, it would be helpful to the readers to view the treatments and draw their own conclusions. In addition, the study does not report the individual ages (ranging from 18 to 70 years), demographics, or body mass indices (BMIs) of the 24 patients enrolled, or, perhaps, mean ages and BMIs of the 3 surgical subgroups. In our center we are seeing a subcohort of often younger patients with painful knee OA but only mild-moderate obesity (BMI 30–35) who would qualify for the less-invasive LAGB by recent FDA guidelines [6], but insurance companies are often unwilling to pay for the surgery. Thus, we are currently piloting a prospective study for patients with mild-moderate obesity (BMI 30–35) and severe pain from KOA, evaluating the effectiveness of LAGB that has shown retrospective promise for such knee pain with minimal risks [7–9]. In conclusion, this study supports recent arguments [10–12] that weight loss from bariatric surgery can provide a significant improvement in patient-reported KOA-related pain and disability. In addition to the mechanical load reduction, each of the three surgical methods may have distinct biochemical and metabolic consequences on knee OA—as other authors have begun to explore the role of inflammation and joint biomarkers from adipose tissue [13]. Such investigation will only further our understanding of the interplay between mechanical and cytokine-driven effects of obesity on knee pain and function.

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 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)0.001
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 score0.130
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.178
Teacher spread0.174 · 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 teacher head, 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
Published2013
Admission routes1
Has abstractyes

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