Modeling intraindividual change over time in the absence of a “Gold Standard”
Bibliographic record
Abstract
Looking at intra-individual change over time in a particular phenomenon may present some methodological challenges. The aim of this report was: 1. To show the effect of independent classification errors on the estimation of incidence and remission rates. 2. To show how a logitbased time-specific latent variables model can be used to model two distinct components of intraindividual change over time in the absence of a "gold standard", namely: (a) the continuity and discontinuity in the latent states over time; and (b) the strength of the association between the time-specific latent variables. 3. To illustrate this model using data on physical aggression from a representative sample of Canadian children assessed at 8-9 years of age and then again two years later at 10-11 years of age. The results showed that classification errors can yield either gross under or over estimates of the true incidence and remission rates. Furthermore, remission was far more sensitive than incidence to classification errors whereas incidence varied more drastically than remission depending on the amount of classification errors. We found that there was no association in the region off the main diagonal of the transition probability matrix beyond that expected by chance alone. In general, the stability of a 8-9 year-old child's latent physical aggression status (i.e., low-, medium- or high-aggressive) did not depend on its severity. Furthermore, the likelihood of changing from one latent physical aggression status to another was generally equal to the one of changing from the latter to the former.
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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 | 0.069 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".