Identifying Early Developmental Profiles in Children with FXS: A Retrospective Home Videos Analysis
Bibliographic record
Abstract
One of the major difficulties concerning Fragile X Syndrome has been early diagnosis enabling early intervention. The obstacle stems from the dismissal of signs that might raise suspicion that the syndrome is present and therefore subsequent diagnosis. The present research aims to validate a methodology employing retrospective home video analysis to explore possible early signs in children with Fragile X Syndrome. As part of this trial the videos of 6 children aged less than 30 months were analysed. We utilized a set formed by three behavioural analysis grids, mainly to analyse social attention, joint attention and sensory-motor development of said children. The retrospective home video analysis demonstrated its effectiveness in early sign identification. We verified that children with FXS had a non-social stimuli preference (e.g., prolonged visual fixation on objects), had difficulties directing attention to social stimuli (e.g., attention and response to name calling), demonstrating impairments in joint attention, and displayed prolong and repetitive interaction with objects as well as positive affective expressions. Our findings indicated that children with FXS seem to be able to discriminate between social and non-social stimuli (e.g., vocalization to people) and presented stereotypes behavior from 0 up till 30 months. Use of home videos is a potentially important methodology in identification of early sign. Identified signs from this study may serve as markers for medical referral to genetic diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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".