Approach to autism spectrum disorder: Using the new DSM-V diagnostic criteria and the CanMEDS-FM framework.
Bibliographic record
Abstract
OBJECTIVE: To review the diagnostic criteria for autism spectrum disorder (ASD) from the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V), and to develop an approach to managing ASD using the CanMEDS- Family Medicine (CanMEDS-FM) framework. SOURCES OF INFORMATION: The DSM-V from the American Psychiatric Association, published in May 2013, provides new diagnostic criteria for ASD. The College of Family Physicians of Canada's CanMEDS-FM framework provides a blueprint that can guide the complex management of ASD. We used data from the Centers for Disease Control and Prevention to determine the prevalence of ASD, and we used the comprehensive systematic review and meta-analysis completed by the UK National Institute for Health and Care Excellence for their guidelines on ASD to assess the evidence for more than 100 interventions. MAIN MESSAGE: The prevalence of ASD was 1 in 88 in 2008 in the United States according to data from the Centers for Disease Control and Prevention. The ASD classification in the fourth edition of the DSM included autism, Asperger syndrome, pervasive developmental disorder, and childhood disintegrative disorder. The new DSM-V revision incorporates all these disorders into one ASD umbrella term with different severity levels. The management of ASD is complex and requires a multidisciplinary team effort and continuity of care. The CanMEDS-FM roles provide a framework for management. CONCLUSION: Family physicians are the key leaders of the multidisciplinary care team for ASD, and the CanMEDS-FM framework provides a comprehensive guide to help manage a child with ASD and to help the child's family.
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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 | 0.037 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".