Serological risk factors associated with arthroplasty complications
Bibliographic record
Abstract
Intro: Post-operative complications can have a devastating impact on patients' quality of life. Peri-operative bloodwork is often routinely conducted and may predict post-operative complications. The purpose of this study is to understand if pre- or post-operative serology is associated with post-operative complications arising from hip and knee arthroplasty. Methods: A retrospective chart review of 383 participants with post-operative total hip and knee arthroplasty complications was conducted. One hundred and forty-one participants with post-operative complications were included and compared to a control group of 120 participants. Patient, surgical, and pre- and post-operative serological data were collected. L2-regularized logistic regression was utilized to assess whether identified factors independently predicted post-operative complications. Results: The complications group had higher age, male sex, presence of osteoporosis, Charlson Comorbidity Index and bilateral arthroplasties compared to the control group (p < 0.05). Pre-operatively, the complications group had a lower hemoglobin value (p = 0.027; OR = 0.64) and higher Basophil Count (p = 0.012, OR = 2.23) and Monocyte-Lymphocyte ratio (p = 0.003, OR = 2.23), higher rates of undergoing a general anesthetic (p = 0.019, OR = 0.43) and psychiatric illness (p = 0.001, OR = 3.07). Post-operatively, the complications group had lower WBC (p = 0.040, OR = 0.55) and Neutrophil counts (p = 0.001, OR = 0.45), increased Eosinophil count (p = 0.018, OR = 2.36), increased age (p = 0.034, OR = 2.06) and BMI (p = 0.049, OR = 1.74), having postoperative anticoagulation other than ASA (p = 0.000, OR = 3.99). Conclusion: We identified multiple peri-operative patient, surgical, and serological risk factors for developing post-operative complications following hip and knee arthroplasty. These markers could be prioritized for monitoring high-risk patients for possible intervention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".