LIKELY BACTERIAL ACUTE CERVICAL LYMPHADENITIS IN CHILDREN: FACTORS PREDICTIVE OF FAVORABLE OUTCOME
Bibliographic record
Abstract
Abstract BACKGROUND Cervical lymphadenitis is frequent in the paediatric population and usually the result of infectious agents. Management of likely bacterial acute cervical lymphadenitis (LBACL) mainly relies on expert advice and may vary widely from antibiotherapy to surgery as no official guidelines have been published over the last decade. OBJECTIVES To identify factors associated with favorable outcome in the management of LBACL in children in order to create a decision algorithm and its further evaluation. DESIGN/METHODS This retrospective observational study was based on the review of medical records of patient from 1 month to 18 years old who have consulted for LBACL between July 2010 and July 2015 at a tertiary care paediatric center. LBACL were identified using electronic record databases (hospitalization, emergency). Patients were included if they had acute (≤10 days) episode of unilateral cervical mass of which the final diagnosis was LBACL. Exclusion criteria were: mycobacterial adenitis, Kawasaki disease, Cat-scratch disease, bilateral cervical lymph node involvement, congenital malformation, immunodeficiency or underlying neoplasia. Favorable evolution was defined as outpatient treatment or hospital stay of 48 hours or less without surgical drainage. To identify factors at initial consultation predictive of a favorable outcome, we performed univariate logistic regression models with several potential independent covariates, including, among others, age (years), size of lymph node (mm), fever (38,5°C), an antibiotic use prior to consultation, fluctuation, absolute white blood cell count (x10^9/L), and purulent material at ultrasonography (yes/no). RESULTS Our final study cohort was composed of 166 patients with a mean age of 4,5 years (3,5SD) and 62% male. Ultrasound was obtained in 139(83,7%) patients and cervical tomodensitometry in 31(18,7%). Surgical drainage was performed in 35(21,1%). Overall, 68(41,0%) patients presented a favorable evolution from which 27(16,9%) were treated as outpatient (figure1). Factors associated with favorable outcome were (OR; 95%CI): age (1.17; 1.06–1.29; p=0.002), absolute white blood cell count (0.91; 0.87–0.96; p=0.001), no antibiotic use prior to consultation (0.26; 0.07–0.92; p=0.037) and absence of purulent material on ultrasound (0.07; 0.02–0.29; p<0.001). Size of lymph node (0.98; 0.96–1.00; p=0,057) or fluctuation (0.71; 0.21–2.39; p=0.57) did not achieved statistical significance. CONCLUSION Older patients without prior antibiotic use, those with lower absolute white blood cell count and no purulent material on ultrasound seem to better evolve than other children with likely bacterial acute cervical lymphadenitis. A decision algorithm to identify patients eligible for conservative management should include those predictive factors.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".