Reduced Daily Dosing of Cefazolin with Probenecid for the Outpatient Treatment of Cellulitis in Children
Bibliographic record
Abstract
independent of an ASD diagnosis.Children with ASD and ADHD represent a specific subtype of ASD, with identifiable characteristics.METHODS/ ANALySIS: A retrospective chart review was conducted.Information gathered included sex, age, IQ, communication profile, Autism Diagnostic Observation Schedule, Autism Diagnostic Interview, adaptive skills and Conners Parent Rating Scale subscale scores.Hierarchical linear models were used to estimate the effect of sex and diagnosis on raw Conners index scores, after controlling for age and adjusting for the correlation among individuals of the same family.An interaction between group and sex was introduced into the model in order to estimate within group gender effect and the effect of diagnosis within each gender.RESULTS: Data from 72 children diagnosed with autism were analyzed.Of these, 15 (20.8%) met the DSM-IV criteria for ADHD, with 2 (13.3%) meeting criteria for the hyperactive impulsive profile, 7 (46.7%) the inattentive profile and 6 (40%) meeting the criteria for the combined profile.When using a less conservative cutoff, an additional 39 (54%) children diagnosed with ASD demonstrated at least six symptoms of hyperactivity, impulsivity and or inattention.Only 18 (25%) children diagnosed with ASD displayed no symptoms of ADHD.Girls with ASD were more likely to be hyperactive and impulsive whilst unaffected sibling males were inattentive.SIGNIFICANCE: These data suggest that there are at least three different subtypes to describe the presentation of ADHD symptoms in children diagnosed with ASD.The three subtypes are the asymptomatic, sub clinical and a clinical group.This adds strength to the dimensional approach to classifying and interpreting symptoms in children diagnosed with autism.This challenges the present DSM-IV criteria which fail to recognize the coexistence of ADHD in ASD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".