Using a Common Form for Consistent Collection and Reporting of FASD Data from Across Canada: A Feasibility Study.
Bibliographic record
Abstract
BACKGROUND: This study was undertaken to determine the feasibility of collecting information on individuals newly diagnosed with Fetal Alcohol Spectrum Disorder (FASD) in multi-disciplinary diagnostic programs across Canada. OBJECTIVE: To determine the frequencies of specific diagnoses within the spectrum, the frequencies and patterns of specific functional deficits, and the range of recommendations made for intervention and management for children and adults. METHODS: All qualifying clinics in Canada were invited to join this project and complete questionnaires on the patients that were seen during the research period. RESULTS: Over half of all clinics participated (25/45) and submitted the information requested on 307 individuals, ranging in age from 1 to 42 years. Two hundred and eighty-nine individuals had a diagnosis of FASD and were analysed further. The percent of individuals with Fetal Alcohol Syndrome was 2.1% of those with FASD diagnoses, which was lower than expected based on the literature. The level of disability among the entire FASD was always significant with at least 3 domains measured as severely impaired via the criteria for diagnosis but almost one-quarter were extremely disabled with 6 of a possible 9 brain domains measured significantly impaired. No specific patterns of functional disability were found to represent any significant subgroup of the patients. An average of 13 new recommendations for intervention and management were made for each patient in health, mental health, social services, and education. CONCLUSION: Although this was a pilot study with a relatively small sample, it is the largest collection of cases of FASD from multiple sites in one country ever published to our knowledge. It illustrates that important patient information can be collected across clinical programs considering the diagnosis of FASD but only with financial support for time and personnel. Using the methodology of a common data form, consistent data collection can be achieved and patterns and trends can be identified that can help with assuring consistency in diagnosis and with planning for improved patient outcomes.
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 | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".