G224 A systematic review to determine if undernutrition is prognostic of infection complications in children following surgery
Bibliographic record
Abstract
Background Hospitalisation has a major impact on the nutritional status of children1. Although incidence of undernutrition has been reported, fewer studies have investigated its effect on patient outcome following paediatric surgery. Review methods Key electronic bibliographic and research databases were searched. Inclusion criteria were studies in children <18 years evaluating preoperative nutritional status and reporting postoperative infection complications. Quality assessment was performed using Newcastle-Ottawa Scale2 for cohort and case-control studies. Appraisal and data extraction were performed independently by two reviewers. Effect estimates and 95% confidence intervals were extracted or calculated using Chi Square or Fisher’s Exact tests as appropriate. Results Two reviewers screened a total of 1108 references. Ten cohort and two case-control studies using a plethora of nutritional assessments were included in the review. Postoperative infection complications reported were either combined or individual e.g. wound infection. Quality of the evidence was judged as low in the majority of studies, with two of moderate and two of very low quality. Direct comparison between studies was not possible due to clinical and diagnostic heterogeneity. Direction of effect on univariate analysis was suggestive of a relationship between undernutrition and postoperative infection complications. Conclusion The lack of a consistently applied method of nutritional assessment, combined with small sample sizes, makes it difficult to draw strong conclusions. There is tentative low quality evidence suggesting undernutrition may be predictive of combined infection complications following surgery in children, but insufficient evidence to determine if this relationship persists when considering specific infection complications. Larger studies, using gold-standard nutritional assessment, and designed to investigate undernutrition with outcome are warranted to investigate this relationship further. References Pichler J, Hill SM, Shaw V, Lucas A. Prevalence of undernutrition during hospitalisation in a children’s hospital: what happens during admission? Eur J Clin Nutr 2014; 68:730–735 Wells GA, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp, 2011
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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 | 0.011 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".