ThP7.11 - Does Hypoalbuminaemia Increase the Risk of Surgical Site Infection in Neck of Femur Fracture Patients? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Aim Serum albumin plays an important role in physiological and inflammatory haemostasis; low serum levels are linked with an increased incidence of surgical site infections (SSI). Although this has been demonstrated in spinal and elective arthroplasty surgeries, there is a paucity of evidence with regard to the effect of low preoperative serum albumin on rates of SSI following surgery for adult patients suffering from traumatic, acute hip fractures. Method A systematic review was conducted using the PRISMA guidelines. Four databases were searched for randomised controlled trials, cohort and case-controlled studies. The risk of bias was assessed using the Newcastle-Ottawa Score (NOS). Data was collected and pooled using RevMan Web Software. Results were reported as Odds Ratio (OR) with 95% Confidence Intervals (CI) and statistical significance of p <0.05. An Inverse-Variance model was used in meta-analysis. Results Six retrospective studies (five cohorts, one case-control) with a total of 43,059 patients were included. 45.3% (n=19,496) had low serum albumin (<3.5 g/dL). Hypoalbuminemia was associated with a significantly higher risk of any form of SSI (OR 1.25, p=0.008) and deep SSI (OR 1.76, p=0.05). There was no statistical significance between hypoalbuminemia and the incidence of superficial SSI (OR 1.06, p=0.77). Organ-space SSI was associated with hypoalbuminemia, albeit only one study reported this with poor statistical significance (OR 8.74, p<0.054). Conclusion Hypoalbuminemia increases the risks of most forms of surgical site infections; superficial and deep infections. There is a weak conclusion to draw between incidence of deep-space organ infections and low serum albumin.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".