Albumin and surgical site infection risk in orthopaedics: a meta-analysis
Bibliographic record
Abstract
BACKGROUD: Surigical site infection has been a challenge for surgeons for many years, the prevalence of serum albumin <3.5g/dL has been reported to be associated with increased orthopaedic complications. However, the prognostic implications and significance of serum albumin <3.5g/dL after orthopaedic surgeries remain ambiguity. In this study, we performed a meta-analysis to access the predictive value of serum albumin level on SSI. METHODS: A basic data search was performed in PubMed and Web of Science, in addition, references were manually searched. All of the observational studies contained preoperative albumin, outcomes of SSI or valuable data that could be abstracted and analysed for meta-analysis in orthopaedics. All of the studies were assessed using the classic Newcastle Ottawa Scale (NOS). They conformed to critical quality evaluation standards, and the final data analysis was performed with RevMan 5.2 software. RESULTS: = 68 %). No publication bias occurred based on two basically symmetrical funnel plots. CONCLUSION: Our meta-analysis demonstrated that an albumin level <3.5 g/dL had an almost 2.5 fold increased risk of SSI in orthopaedics, although this conclusion requires well-designed prospective cohort studies to be confirmed further.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.036 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".