Impairment<i>versus</i>deficiency in neuropsychological assessment: Implications for ecological validity
Bibliographic record
Abstract
Neuropsychological test interpretation has relied on pre- and postmorbid comparisons, as exemplified by the use of demographically adjusted normative data. We argue that, when the assessment goal is to predict real-world functioning, this interpretive method should be supplemented by "absolute" scores. Such scores are derived from comparisons with the general healthy adult population (i.e., demographically unadjusted normative data) and reflect examinees' current ability, that is, the interaction between premorbid and injury/disease-related factors. In support of this view, we found that substantial discrepancies between demographically adjusted and absolute scores were common in a traumatic brain injury sample, especially in participants with certain demographic profiles. Absolute scores predicted selected measures of functional outcome better than demographically adjusted scores and also classified participants' functional status more accurately, to the extent that these scores diverged. In conclusion, the ecological validity of neuropsychological tests may be improved by the consideration of absolute scores. (JINS, 2009, 15, 94-102.).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".