Role of Raised C-Reactive Protein (CRP), Total Leukocyte Count (TLC) and Differential Leukocyte Count (DLC) in Odontogenic Space Infections
Bibliographic record
Abstract
Objectives: Serum C- Reactive Protein (CRP), Total Leukocyte Count (TLC) and Differential Leukocyte Count (DLC) measurements have been effectively utilized as a diagnostic marker to study infection in many disciplines. However, the usefulness of these is scarce in maxillofacial infections. This study aimed to assess the frequency of raised CRP, TLC and DLC in patients with odontogenic space infections. Materials and Methods: This descriptive cross-sectional study was conducted at the Department of Oral and Maxillofacial Surgery, de'Montmorency College of Dentistry, Punjab Dental Hospital, Lahore, over a span of six months i.e., from August 2019 to February 2020. A total of 181 patients (age range from 18 to 70 years) with facial space infection of odontogenic origin only were recruited. Patients suffering from chronic disease like diabetes mellitus and chronic renal failure, and pregnant women were excluded. Patient's venous blood samples were collected preoperatively and sent to the hospital laboratory for measurement of CRP, TLC and DLC levels. Descriptive statistics (mean±standard deviation and frequencies) were performed using SPSS version 21. Results: Amongst the 181 participants, the mean age of 44.43 ± 14.68 years were recorded of which 113 (62.43%) were male and 68 (37.57%) were females with male to female ratio 1.7:1. The mean values of CRP were 3.52±1.23 mg/L, TLC were 13670±1890 cells/mm3 and DLC were 8.54±2.39 × 109 /L. Moreover, the frequency of CRP levels was raised in 100%, TLC in 71.82% and DLC in 85.08% patients with odontogenic space infections. Conclusion: This study concluded that raised CRP levels are more indicative of the clinical severity of the infection compared to TLC and DLC. Thus, CRP was found to be an effective biomarker in patients with facial space infection of odontogenic origin
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".