Indirect language therapy for children with persistent language impairment in mainstream primary schools: outcomes from a cohort intervention.

BACKGROUND: A manualized language therapy developed via a randomized controlled trial had proved efficacious in the short-term in developing expressive language for mainstream primary school children with persistent language impairment. This therapy had been delivered to a predetermined schedule by speech and language therapists or speech and language therapy assistants to children individually or in groups. However, this model of service delivery is no longer the most common model in UK schools, where indirect consultancy approaches with intervention delivered by school staff are often used. AIMS: A cohort study was undertaken to investigate whether the therapy was equally efficacious when delivered to comparable children by school staff, rather than speech and language therapists or speech and language therapy assistants. METHODS & PROCEDURES: Children in the cohort study were selected using the same criteria as in the randomized controlled trial, and the same manualized therapy was used, but delivered by mainstream school staff using a consultancy model common in the UK. Outcomes were compared with those of randomized controlled trial participants. OUTCOMES & RESULTS: The gains in expressive language measured in the randomized controlled trial were not replicated in the cohort study. Less language-learning activity was recorded than had been planned, and less than was delivered in the randomized controlled trial. Implications for 'consultancy' speech and language therapist service delivery models in mainstream schools are outlined. CONCLUSIONS & IMPLICATIONS: At present, the more efficacious therapy is that delivered by speech and language therapists or speech and language therapy assistants to children individually or in groups. This may be related to more faithful adherence to the interventions schedule, and to a probably greater amount of language-learning activity undertaken. Intervention delivered via school-based 'consultancy' approaches in schools will require to be carefully monitored by schools and SLT services.

Performance reassessment of a real-time seizure-detection algorithm on long ECoG series.

PURPOSE: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed-loop studies to block seizures in humans. METHODS: Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off-line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated. RESULTS: Fourteen subjects met inclusion criteria. Generic algorithm "relative sensitivity" for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7-66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7-4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs. CONCLUSIONS: The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed-loop therapy studies.