The Combination of Depression and Obesity Is Associated With Increased Incidence of Subsequent Total Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Compare the incidence of total knee arthroplasty (TKA) within the first 5 years after knee OA diagnoses between matched groups of individuals with or without comorbid diagnoses of obesity and/or depression. We hypothesized that the greatest incidence of TKA within 5 years of OA diagnosis would be in the cohort of individuals with combined obesity and depression. METHODS: The PearlDiver Mariner Ortho157 database was used to identify four cohorts of individuals with knee OA based on diagnosis codes that were matched by age, sex, and Charlson Comorbidity Index: a group without diagnoses associated with depression or obesity (Control), those with obesity but not depression (Obesity), those with depression but not obesity (Depression), and those with diagnoses of both obesity and depression (Depression+Obesity). The incidence of subsequent TKA within the first 5 years after the index OA diagnosis were compared between the four matched cohorts. RESULTS: Each cohort was comprised of 274,403 unique individuals (180,563 females, 93,840 males; age=55±7 y). The incidence of TKA was greatest for the Depression+Obesity group (11.9%) when compared to the Control group (8.3%, p<0.0001, RR=1.43 [95%CI:1.41,1.45]), the Obesity group (10.2%, p<0.0001, RR=1.13 [95%CI:1.11,1.14], p<0.0001) or Depression (7.8%, p<0.0001, RR=1.53 [95%CI:1.50,1.55], p<0.0001). CONCLUSION: The incidence of subsequent TKA was greatest for those with the combination of obesity and depression when compared to the Control group and those with individual diagnosis of obesity or depression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".