The impact of antibiotics on clinical response over time in uncomplicated cellulitis: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Antibiotic treatment of uncomplicated cellulitis is highly variable with respect to agent, dose, and route of administration. As there is uncertainty about optimal/appropriate time to reassess, we aimed to assess time to clinical response. METHODS: We conducted a systematic review of randomized controlled trials reporting clinical response of uncomplicated cellulitis to antibiotic treatment over multiple timepoints. PubMed, Embase, CENTRAL, WHO ICTRP, and clinicaltrials.gov were searched from inception to June 2021 without language restrictions. The primary outcome was time to clinical response. Other outcomes were components of clinical response (pain, severity score, redness, edema measured at ≥ 2 timepoints) and the proportion of patients with treatment failure. We performed a pooled estimate of the average time to clinical response together with 95% confidence intervals using a random effects model. RESULTS: = 76%). The response to treatment for specific components was as follows: ~ 50% reduction of pain and severity score by day 5, a ~ 33% reduction in area of redness by day 2-3, and a 30-50% reduction of proportion of patients with edema by day 2-4. Treatment failure was variably defined with an overall failure rate of 12% (95%CI 9-16%). CONCLUSION: The best available data suggest the optimal time to clinical reassessment is between 2 and 4 days, but this must be interpreted with caution due to considerable heterogeneity and small number of included studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it