Modeling Interinformant Agreement in the Absence of a “Gold Standard”
Bibliographic record
Abstract
Epidemiological surveys of child and adolescent mental disorders often rely on multiple informants to get a complete diagnostic picture. A consistent finding in the literature is that different informants often do not identify the same children as being disordered. However, because current strategies for estimating interinformant agreement often involve categorizing children using less than perfectly sensitive and/or specific symptoms, biased estimates of interinformant agreement are likely. The aim of this report was to illustrate how latent class analysis (LCA) can be used to model interinformant agreement in the absence of a "gold standard". The proposed model consists of informant-specific latent variables each made up of two or more latent classes corresponding to different levels of symptomatology. Unlike most previous applications of LCA this model allows us to model the extent to which the prevalence of the disorder is the same across informants; and, in addition, the association between informants. The data set comes from a prospective longitudinal study of 2,264 children from Quebec (1,155 boys and 1,109 girls). In grade 2, teachers and mothers independently rated each child on three physical aggression behavior symptoms. We satisfactorily accounted for the cross-classification of the behavior symptoms by postulating the existence of two latent variables--one for each informant each made up of three latent classes of children: low-, medium-, and high-aggressive. The results showed that the prevalence of low- and medium-aggressive children in the population differed from teacher to mother, but that the prevalence of high-aggressive children did not. We found that the association between teacher and mother was large and positive and did not vary according to the child's physical aggression state or gender; in contrast, the association between physical aggression and gender was not the same for mother and teacher. Limitations and other potential applications of the proposed model are discussed.
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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 | 0.162 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".