Neutrophil to lymphocyte ratio in odontogenic infection: a systematic review
Bibliographic record
Abstract
BACKGROUND: We conducted this systematic review to compile the evidence for the role of neutrophil to lymphocyte ratio (NLR) in odontogenic infection (OI) and to determine whether NLR is elevated in patients with OI. This was done to aid physicians in better understanding this condition for clinical management. METHODS: The search was conducted on PubMed, Scopus, and Web of Science libraries on March 30, 2023. Two reviewers independently screened the studies using Endnote software. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the studies. RESULTS: A total of nine studies were included in the review. Among patients with OI, positive and statistically significant correlations of NLR were seen with more severe disease, a prolonged hospital stay, postoperative requirement of antibiotics, and total antibiotic dose needed. In the receiver operating characteristics (ROC) analysis, the optimum cut-off level of NLR was 5.19 (specificity: 81, sensitivity: 51). In addition, NLR was correlated with preoperative fever (p = 0.001). Among patients with Ludwig's Angina, NLR could predict disease severity and length of stay in the hospital (p = 0.032 and p = 0.033, respectively). In addition, the relationship between the NLR and mortality was statistically significant (p = 0.026, specificity of 55.5%, and sensitivity of 70.8%). Among patients with severe oral and maxillofacial space infection, a positive correlation was found between IL-6 and CRP with NLR (rs = 0.773, P = 0.005 and rs = 0.556, P = 0.020, respectively). Also, a higher NLR was considered an essential predictor of organ involvement (P = 0.027) and the number of complications (P = 0.001). However, among diabetes mellitus (DM) patients afflicted with submandibular abscesses, NLR had no association with therapeutic response. CONCLUSIONS: Many people around the world suffer from OI, and a cheap and fast biomarker is needed for it. Interestingly, inflammation plays a role in this infection, and elevated NLR levels can be a good biomarker of inflammation and, as a result, for OI progression.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".