Bibliographic record
Abstract
Abstract Significant changes have occurred in the field of autism, now referred to as Autism Spectrum Disorder (ASD). Despite what we have learned about autism in the past 30 years, the medical community still has more to uncover. Scientists from the fields of neuroimaging, genetics, epidemiology, cognitive neuroscience, and immunology have all joined together in the search for potential causes of autism. Autism Spectrum Disorder aims to provide readers with the knowledge they need to derive maximal benefit from clinical rotations related to ASD and for successful preparation for board examinations. This book is designed as an easy-to-use, clinically oriented, evidence-based guide for trainees and early stage clinicians. It was written with medical students, graduate students and interns in psychology, residents and fellows in neurology, psychiatry, and pediatrics, and those clinicians and researchers who have recently completed training in mind. It is intended to be read and understood during a 4- to 12-week rotation focused on autism spectrum disorder and potentially other neurodevelopmental disorders and also during review and preparation for board examinations that occur throughout professional school, postgraduate training, and for specialty-board certification after training is completed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.163 | 0.077 |
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".