Measurement of educational attainment in school‐aged children born preterm
Bibliographic record
Abstract
The rigorously designed study by Johnson et al.1 sets out to assess the utility of the Teacher Academic Attainment Scales (TAAS) as an educational outcome measure in a study of children born preterm and term born controls at school-age. Several papers have been published by Johnson et al.2 on the results of the EPICure study, and have reported on neuropsychological outcomes and academic attainment of children born extremely preterm and the prevalence of their needs for special educational in middle childhood. This current paper proposes the TAAS as an adequate way to replace standardized measures of educational outcomes, which are lengthy and costly to administer. Specifically, these authors aim to show that a brief teacher-rating of academic attainment could be a cost- and time-efficient alternative to traditional standardized testing. Several important issues are brought forth by this paper. First, the importance of scholastic performance as an outcome for children born preterm cannot be overemphasized. Assessment of need for special education provides a functional marker for scholastic performance which complements the complexity of the detailed neuropsychological assessment. Both provide an important measurement of the risk for ongoing impairment in educational outcomes.2 Second, this study identifies the classroom teacher as an essential component in the assessment of academic risk and has provided evidence of acceptable test–retest reliability, and concurrent and predictive validity between teacher assessment and standardized tests to prove the point. This pragmatic focus on the classroom teacher as a source for quantifying educational attainment makes the inclusion of these data more feasible and thus more likely in future studies.1 Finally, it is important to bear in mind the different purposes for measurement in clinical practice and research; i.e. description and screening tools determine the frequency and presence/absence of a particular condition and evaluative tools are utilized for treatment planning and setting goals.3 While detailed neuropsychological assessments are indeed costly, their importance lies in educational planning and, hopefully, in measuring change with intervention. The advantage and utility of well validated measures such as the TAAS is that they allow us to measure more of the whole picture of attainment of high-risk populations, such as those born preterm, in large-scale studies.4 Future studies that include the TAAS as a measure of educational outcome over time will be important to better interpret the findings of this paper.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".