Impact of Atopic Dermatitis (Eczema) and Its Treatment on the Risk of Adverse Events Following Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD), also known as eczema, is a highly prevalent, chronic inflammatory skin condition. The perioperative outcomes of patients with AD after total knee arthroplasty (TKA) have not been characterized. METHODS: Adult patients who underwent TKA were identified in the PearlDiver administrative database. After matching based on patient characteristics, 90-day adverse events and 5-year revisions were compared by multivariable analyses and log-rank tests, respectively. Patients with atopic dermatitis were then stratified by medication status for repeated analysis between resultant subcohorts. RESULTS: Relative to age, sex, and comorbidity matched patients without AD, those with AD had increased odds of aggregated adverse events (OR = 1.36), pneumonia (OR = 2.07), urinary tract infection (UTI, OR = 1.77), and emergency department (ED) visits (OR = 1.70) (P < 0.0001 for each). Those on medication for moderate-to-severe disease had similar associations as the primary analysis. Those not on medications were similar, but not found to have elevated odds of pneumonia. 5-year revisions were not markedly different. CONCLUSION: TKA patients with AD were at increased odds of pneumonia, UTI, and ED visits, but these risks were not exacerbated by immunosuppressive medications. Surgeons who are managing patients with AD for TKA should be vigilant but reassured by overall similar 5-year survival to revision.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".