Assessment of neuropsychological trajectories in longitudinal population-based studies of children: Table 1
Bibliographic record
Abstract
This paper provides a strategy for the assessment of brain function in longitudinal cohort studies of children. The proposed strategy invokes both domain-specific and omnibus intelligence test approaches. In order to minimise testing burden and practice effects, the cohort is divided into four groups with one-quarter tested at 6-monthly intervals in the 0-2-year age range (at ages 6 months, 1.0, 1.5 and 2.0 years) and at annual intervals from ages 3-20 (one-quarter of the children at age 3, another at age 4, etc). This strategy allows investigation of cognitive development and of the relationship between environmental influences and development at each age. It also allows introduction of new domains of function when age-appropriate. As far as possible, tests are used that will provide a rich source of both longitudinal and cross-sectional data. The testing strategy allows the introduction of novel tests and new domains as well as piloting of tests when the test burden is relatively light. In addition to the recommended tests for each age and domain, alternative tests are described. Assessment methodology and knowledge about child cognitive development will change over the next 20 years, and strategies are suggested for altering the proposed test schedule as appropriate.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".