Infectious diseases specialist management improves outcomes for outpatients diagnosed with cellulitis in the emergency department: a double cohort study
Bibliographic record
Abstract
Three hospital emergency rooms (ERs) routinely referred all cases of cellulitis requiring outpatient intravenous antibiotics, to a central ER-staffed cellulitis clinic. We performed a retrospective cohort study of all patients seen by the ER clinic in the last 4months preceding a policy change (ER management cohort [ERMC]) (n=149) and all those seen in the first 3months of a new policy of automatic referral to an infectious disease (ID) specialist-supervised cellulitis clinic (ID management cohort [IDMC]) (n=136). Fifty-four (40%) of 136 patients in the IDMC were given an alternative diagnosis (noncellulitis), compared to 16 (11%) of 149 in the ERMC (P<0.0001). Logistic regression-demonstrated rates of disease recurrence were lower in the IDMC than the ERMC (hazard ratio [HR], 0.06; P=0.003), as were rates of hospitalization (HR, 0.11; P=0.01). There was no significant difference in mortality. Automatic ID consultation for cellulitis was beneficial in differentiating mimickers from true cellulitis, reducing recurrence, and preventing hospital admissions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".