Nutritional Indicators of Bone Nonunion: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Bone nonunion remains a clinical challenge in orthopedic surgery with significant impacts on mental and physical wellbeing for patients. There are several previously established risk factors of nonunion that are connected to nutrition, but this has yet to be substantially explored. This review seeks to assess all studies that present associations between nutrition and nonunion to understand the potential for clinical relevance in nonunion prevention. Methods: Case–control and cohort studies comparing nonunion risk based on nutritional factors were gathered through PubMed in July 2024. Data were extracted with dual verification through Covidence and assessed for bias using the Newcastle–Ottawa Scale. Results: A total of 21 studies were included in this literature review. Vitamin D deficiency was a significant risk factor of nonunion in six studies and not significant in six other studies. Albumin was significant in three of the five studies addressing this lab value. Iron deficiency anemia was significant in a study assessing its impact on nonunion. Calcium was not significant in the one study mentioned. ICD-10-coded malnutrition was significant in one of the two studies. Sarcopenia, nutritional care plans, and dietitian-diagnosed malnutrition were statistically significant clinical indicators for predicting nonunion, but food insecurity was insignificant. Conclusions: Vitamin D, calcium, albumin, iron deficiency anemia, sarcopenia, and clinically diagnosed malnutrition have all been associated with an increased risk of nonunion in observational studies and should be considered when preventing nonunion development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".