The preliminary research on unifying different neuropsychological scales under the guideline of item response theory
Bibliographic record
Abstract
Objective To explore the probability to unify the scores of different neuropsychological scales under the guideline of item response theory(IRT). Methods Two hundred and three old individuals with a mean age of (80.86±9.52) years were enrolled in this study, including 101 normal individuals and 102 patients with mild cognitive impairment (MCI). The data of cognitive ability assessed using MoCA and MMSE scales were collected and analyzed according to IRT with Stata 14.0 software.The population, qualitative data, and scaled data were analyzed by Hybrid model, 2PL model, and GRM model, respectively.Finally, model fitting was conducted in these data.We used test characteristic curves(TCC)to calculate corresponding theta values of the scores of MMSE and MoCA, and draw cartograms after fitting the model. Results The data of MoCA and MMSE conformed to the characteristics of unidimension.The scores of MoCA and MMSE were 26.37±2.63 and 29.06±1.32, respectively, in the normal group, and those were 21.57±3.47 and 26.62±2.89, respectively, in the MCI group.The Log likelihood for the fitting model of MoCA and MMSE was -1 670.9012 and -773.9797, respectively.The TCC indicated that the score of MoCA was lower than that of MMSE at the same level of theta value.The TCC of the two scales were both steep at first, and then became flat.In evaluating the cognition ability, the MoCA score was more precise than MMSE score, and MMSE score was easier to show ceiling effect than MoCA score. Conclusions Under the guideline of item response theory, we can unify the scores of two scales in order to make the scores of two scales fit with each other and be in common use during study and research. Key words: Cognition disorders; Neuropsychological scale; Item response theory
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".