A Systematic Review of Algorithms for Identifying Pediatric Neurodevelopmental Outcomes
Bibliographic record
Abstract
PURPOSE: Investigating pediatric neurodevelopmental outcomes (NDO) in studies using secondary data is often challenging due to heterogeneous clinical definitions and medical coding systems. This study aims to identify the algorithms used to define NDO in studies using electronic healthcare data through a systematic literature review. METHODS: A search strategy was developed to identify studies on NDO that describe phenotype algorithms from January 1, 2010, to March 10, 2025. The search strategy included terms to identify studies containing algorithms for NDO as an outcome, routinely collected healthcare data, epidemiologic designs likely to incorporate algorithms, and pregnant individuals and/or infants/children. Two independent reviewers assessed eligibility criteria and performed data extraction, with inconsistencies reviewed by a third reviewer. Descriptive statistics were used to summarize categorical and continuous variables appropriately. RESULTS: The review included 156 publications that implemented algorithms for NDO, with 18 of these studies validating the outcomes. Most publications studied autism spectrum disorder (ASD) (n = 103, 65.6%) and attention deficit hyperactivity disorder (ADHD) (n = 72, 45.9%) either as a single outcome or as a composite. CONCLUSIONS: Instead of presenting NDO as a composite outcome, it is recommended to present multiple single outcomes. Validated outcomes in data from Nordic countries demonstrate a high positive predictive value when using one code for diagnoses, while more complex algorithms are required for US data. Clearly detailing and establishing the time of assessment for each NDO is critical to inform valid epidemiological estimates.
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 | 0.057 | 0.245 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.042 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".