SAFETY AND EFFICACY OF OUTPATIENT TOTAL HIP ARTHROPLASTY IN OBESE PATIENTS
Bibliographic record
Abstract
The purpose of our study is to examine the outcome of patients undergoing outpatient total hip arthroplasty with a BMI >35. Case-control matching on age, gender (46% female;54%male), and ASA (mean 2.8) with 51 outpatients BMI≥35 kg/m2 (mean of 40 (35–55)), mean age of 61 (38–78) matched to 51 outpatients BMI<35 kg/m2 (mean of 27 (17–34)) mean age 61 (33–78). Subsequently 47 inpatients BMI≥35 kg/m2 (mean of 40 (35–55)) mean age 62 (34–77) were matched outpatients BMI≥35 kg/m2. For each cohort, adverse events, readmission in 90 days, reoperations were recorded. Rate of adverse events was significantly higher in BMI ≥35: 15.69% verus 1.96% (p=0.039) with 5 reoperations in the BMI≥35 cohort vs 0 in the BMI<35 kg/m2 (p= 0.063). Readmissions did not differ between groups (p=0.125). No significant difference for all studied outcomes between the outpatient and inpatients cohorts with BMI≥35 kg/m2. The most complications requiring surgery/medical intervention (3B) were in the inpatient cohort of patients >35. The prevalence of Diabetes and Obstructive Sleep apnea was 21.6% and 29.4% for BMI>35 compared to 9.8% and 11.8%, for BMI <35, respectively. Severely obese patients have an overall higher rate of adverse events and reoperations however it should not be used a sole variable for deciding if the patient should be admitted or not.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".