69 Acceptability of a Virtual Model for Diagnosis for Fetal Alcohol Spectrum Disorder (FASD)
Bibliographic record
Abstract
Abstract Primary Subject area Community Paediatrics Background A multidisciplinary team is required for diagnosis and recommendations of fetal alcohol spectrum disorder (FASD). The process involves assessments of growth and facial features, a caregiver interview with the physician, assessment of the patient by a psychologist, speech language pathologist and occupational therapist, and a multidisciplinary meeting of the above clinicians, clinic coordinators, school personnel and other support workers. A final meeting is held with the caregiver to debrief on the team findings, diagnosis, and recommendations. A literature search supported the feasibility of a reliable and accurate assessment of patients that adhere to the recommended Canadian FASD diagnostic guideline. As a result, a “Virtual Model for FASD Diagnosis” was developed. Objectives 1. Pilot a project to assess a Virtual Model of FASD Diagnosis; 2. Promote the model, by webinars, to FASD diagnostic teams nationally and internationally; 3. Survey acceptability of the model among webinar attendees. Design/Methods A literature search revealed that teams used virtual platforms for some components of FASD diagnostic process, but a complete virtual process does not exist. Virtual assessment of motor skills domain was not completed because, in the project team’s experience, this domain is rarely impaired. The project leaders developed a model and partnered with two diagnostic teams to complete a small pilot project of 6 patients, using Telehealth and Gotomeetings as a virtual platform to accommodate patients. Patients were scheduled as per the waitlist for each team. Support workers were trained to be with the patient and the caregiver to support any technological aspects, present testing materials, complete growth measurements and photographs for the photographic software for facial measurements. The coordinator scheduled clinician assessments and caregiver interviews; a multidisciplinary team meeting to discuss findings, diagnoses, and recommendations, and a meeting with the caregiver to debrief. A project member analyzed the photographs to measure the sentinel facial features. A survey of the caregivers, clinicians, support workers, and diagnostic team members was conducted to assess the experience, reliability, and feasibility of the virtual model. Webinars of the model were held (one in Alberta, and one for all of Canada, New Zealand, and Australia). A survey of participants’ pre- and post-webinar use of virtual platforms for part or all of the FASD assessment was completed. Results The results of the pilot project survey (Table 1) confirmed the feasibility, acceptability, and reliability of the virtual model of assessment. The caregivers confirmed that the process was rigorous and acceptable. 40% of team members indicated they would not have been present for an in-person meeting, indicating that the virtual format enabled attendance. Webinar surveys indicated a significantly increased interest in completing at least some portions of the assessment virtually. Conclusion Results indicate that a Virtual Model for FASD diagnosis is feasible, reliable, and acceptable. Increased interest in parts of or the whole project was indicated by teams nationally and internationally. Endorsements increased member attendance for team deliberations when virtual.
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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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".