Development of a clinical severity score for preseptal cellulitis in children.
Bibliographic record
Abstract
PURPOSE: There is a need for a valid and reliable method to describe the severity of preseptal cellulitis. METHODS: Items of a scoring system were derived by an expert group and evaluated using a retrospective chart review. The results were used to construct the final Severity Index. Validity and reliability of the Severity Index was evaluated by prospective assessment of 17 children. The Severity Index was compared with a Global Score, a score based on clinical impression. RESULTS: The average Severity Index score was 2.0 for patients treated with oral antibiotics alone and 6.0 for patients treated with intravenous antibiotics. The Severity Index correlated well with the Global Score (Spearman rank correlation coefficient rS = 0.60, P = 0.01). Ranked clinical photographs of preseptal cellulitis correlated moderately to the Severity Index (rS = 0.66, P = 0.02). The Severity Index score after 24 hours of treatment was significantly lower than at presentation (P = 0.004). The agreement between paired Severity Index scores [intraclass correlation coefficient (ICC) = 0.80, P = 0.001] was better than the agreement between paired Global Scores (ICC = 0.45, P = 0.03). CONCLUSIONS: The Severity Index is an objective clinical tool for evaluating severity of preseptal cellulitis in children. It correlates well with clinical constructs for severity and is sensitive to small changes in clinical status. It has better reliability than overall clinical impression. The Severity Index will also be valuable as an outcome measure for future therapeutic trials for preseptal cellulitis in children.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".