Selection Bias Introduced by Neuropsychological Assessments
Bibliographic record
Abstract
OBJECTIVE: Two prospective studies in patient with brain tumours were performed comparing the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The first assessed their feasibility and the second compared their diagnostic accuracy against a four-hour neuropsychological assessment (NPA). The introduction of the NPA decreased accrual and retention rates. We were therefore concerned regarding potential selection bias. METHODS: Ninety-two patients were prospectively accrued and subsequently divided into three categories: a) no NPA required b) withdrew consent to NPA c) completed NPA. In order to quantify any potential bias introduced by the NPA, patient demographics and cognitive test scores were compared between the three groups. RESULTS: There were significant differences in age (p < 0.001), education (p = 0.034), dexamethasone use (p = 0.002), MMSE (p = 0.005), and MoCA scores (p < 0.001) across the different study groups. Furthermore, with increasing involvement of the NPA, patients' cognitive scores and educational status increased, while their age, dexamethasone use, and opioid use all decreased. Individuals who completed the NPA had higher MoCA scores than individuals who were not asked to complete the NPA (24.7 vs. 20.5; p < 0.001). In addition, this relationship held when restricting the analyses to individuals with brain metastases (p < 0.001). CONCLUSIONS: In this study, the lengthy NPA chosen introduced a statistically and clinically significant source of selection bias. These results highlight the importance of selecting brief and well tolerated assessments when possible. However, researchers are challenged by weighing the improved selection bias associated with brief assessments at the cost of reduced diagnostic accuracy.
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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 | 0.112 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".