Influence of temperament on early neurodevelopmental disorders: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Several studies have highlighted the role of temperament as a relevant construct to understand the wide variability observed in neurodevelopmental disorders (NDDs) such as autism spectrum disorder and attention deficit hyperactivity disorder. Some studies have pointed to temperamental traits such as strained control as possible precursors to the development of these disorders. In addition, how temperament influences high-risk populations, as well as intervention programmes aimed at families, has been investigated. METHODS AND ANALYSIS: This paper presents the protocol that will be followed to carry out a systematic review, the objective of which is to know how child temperament is related to the different domains of development in children with NDD or the risk of suffering from it. The search strategy will be implemented in Web of Science (WoS Core Collection), PubMed, ERIC, PsycINFO and Cochrane databases. The risk of bias will be measured by the Newcastle-Ottawa Scale to carry out the integration of the results obtained to synthesis without meta-analysis will be used. This systematic review aims to improve scientific evidence for institutions and professionals and enhance the effectiveness of early care programmes for children with NDD and their families. ETHICS AND DISSEMINATION: No express approval has been sought from any ethics committee because there is no primary data involved and no access to confidential patient information. PROSPERO REGISTRATION NUMBER: CRD42023445173.
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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 | 0.075 | 0.066 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.013 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".