“You can’t touch this”: Delivery of inpatient neuropsychological assessment in the era of COVID-19 and beyond
Bibliographic record
Abstract
Objective: The COVID-19 pandemic is a global health crisis that has created sudden and unique challenges within the field of clinical neuropsychology. Adapting neuropsychology services using teleneuropsychology models (e.g. video or telephone assessments) may not always be a viable option for all providers and settings. Based on the existing teleneuropsychology literature, we propose a “contactless” evidence-based inpatient test battery to be used for in-person assessments amenable to physical distancing. Method: In addition to the proposed test battery, we suggest a decision-making workflow process to help readers determine the appropriateness of the proposed methods given their patients’ needs. Considerations for special populations (i.e. seniors, patients with brain injury, psychiatric patients), feedback, limitations of the proposed physical distancing approach, and future directions are also discussed. Conclusions: Our aim is that the suggested teleneuropsychology-informed battery and model may inform safe and practical neuropsychological inpatient assessments during the COVID-19 pandemic and other situations requiring contact precautions for infection prevention and control.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".