An examination of the quality and performance of the Alda scale for classifying lithium response phenotypes
Bibliographic record
Abstract
OBJECTIVES: The Retrospective Assessment of the Lithium Response Phenotype Scale (Alda scale) is the most widely used clinical measure of lithium response phenotypes. We assess its performance against recommended psychometric and clinimetric standards. METHODS: We used data from the Consortium for Lithium Genetics and a French study of lithium response phenotypes (combined sample >2500) to assess reproducibility, responsiveness, validity, and interpretability of the A scale (assessing change in illness activity), the B scale, and its items (assessing confounders of response) and the previously established response categories derived from the Total Score for the Alda scale. RESULTS: The key findings are that the B scale is vulnerable to error measurement. For example, some items contribute little to overall performance of the Alda scale (eg, B2) and that the B scale does not reliably assess a single construct (uncertainty in response). Machine learning models indicate that it may be more useful to employ an algorithm for combining the ratings of individual B items in a sequence that clarifies the noise to signal ratio instead of using a composite score. CONCLUSIONS: This study highlights three important topics. First, empirical approaches can help determine which aspects of the performance of any scale can be improved. Second, the B scale of the Alda is best applied as a multidimensional index (identifying several independent confounders of the assessment of response). Third, an integrated science approach to precision psychiatry is vital, otherwise phenotypic misclassifications will undermine the reliability and validity of findings from genetics and biomarker studies.
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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 | 0.139 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".