Additional file 1 of New approaches and technical considerations in detecting outlier measurements and trajectories in longitudinal children growth data
Bibliographic record
Abstract
Additional file 1: Supplemental Figure 1. Flow-chart describing data cleaning procedures using the modified BIV detection method (mBIV). Cleaning of age- and sex-standardized WHO weight-for-length z-scores (zWFL) outlier measurements is used as an example to demonstrate the method. Abbreviations: RA, research assistants; SD, standard deviation. Supplemental Figure 2. Methods precision (panel A) and kappa (panel B) for all error types and intensities. Supplemental Figure 3. TARGet Kids! zWFL and CTX zWA and zMUAC clusters obtained via hierarchical clustering using original (without artificial outliers) data. Supplemental Section 1. Definition of error type (how the error is added to the measurement). Supplemental Section 2. Difference between static BIV detection based on fixed outlier removal WHO cut-off values (sBIV) and the modified BIV detection method (mBIV). Supplemental Section 3.Formula definitions as in (1-3). Supplemental Table 1. Summary of anthropometric measurements available for the TARGet Kids! and the CTX trial dataset. Supplemental Table 2. Summary of results from 100 simulation experiments of outlier detection methods applied on the TARGet Kids! Dataset. Expressed as mean (SD) for weight-for-length z-scores. Supplemental Table 3a. Summary of results from 100 simulation experiments of outlier detection methods applied on the CTX Dataset. Expressed as mean (SD) for MUAC z-scores. Supplemental Table 3b. Summary of results from 100 simulation experiments of outlier detection methods applied on the CTX Dataset. Expressed as mean (SD) for weight-for age z-scores. Supplemental Table 4. Sensitivity, specificity, precision and kappa per growth measure and dataset when combing outlier measurement detection methods (Method A and B). Supplemental Table 5. Model fitting parameters for the population average trajectory for the original dataset and for the dataset with outliers of 6 different intensities. Supplemental Table 6. Summary of sensitivity results with 4 outliers per trajectory applied on the TARGet Kids! Dataset.
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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 | 0.010 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.813 | 0.144 |
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".