Correlation or Limits of Agreement? Applying the Bland-Altman Approach to the Comparison of Cognitive Screening Instruments
Bibliographic record
Abstract
BACKGROUND/AIMS: Calculation of correlation coefficients is often undertaken as a way of comparing different cognitive screening instruments (CSIs). However, test scores may correlate but not agree, and high correlation may mask lack of agreement between scores. The aim of this study was to use the methodology of Bland and Altman to calculate limits of agreement between the scores of selected CSIs and contrast the findings with Pearson's product moment correlation coefficients between the test scores of the same instruments. METHODS: Datasets from three pragmatic diagnostic accuracy studies which examined the Mini-Mental State Examination (MMSE) vs. the Montreal Cognitive Assessment (MoCA), the MMSE vs. the Mini-Addenbrooke's Cognitive Examination (M-ACE), and the M-ACE vs. the MoCA were analysed to calculate correlation coefficients and limits of agreement between test scores. RESULTS: Although test scores were highly correlated (all >0.8), calculated limits of agreement were broad (all >10 points), and in one case, MMSE vs. M-ACE, was >15 points. CONCLUSION: Correlation is not agreement. Highly correlated test scores may conceal broad limits of agreement, consistent with the different emphases of different tests with respect to the cognitive domains examined. Routine incorporation of limits of agreement into diagnostic accuracy studies which compare different tests merits consideration, to enable clinicians to judge whether or not their agreement is close.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".