A scoping review and critique of the Input–Output subtyping dimension of spatial neglect
Bibliographic record
Abstract
Spatial neglect is a common and debilitating disorder after stroke whereby individuals have difficulty reporting, orienting, and/or responding to the contralesional side of space. Given the heterogeneity of neglect symptom presentation, various neglect subtypes have been proposed to better characterize the disorder. This review focuses on the distinction between Input neglect (i.e., difficulty perceiving and/or attending to contralesional stimuli) and Output neglect (i.e., difficulty planning and/or executing movements toward contralesional stimuli). Conceptualizations of Input and Output neglect have varied considerably. We provide a novel summary of the terminology, measurement approaches, and neural correlates of these subtypes. A protocol detailing our systematic scoping review strategy is registered on the Open Science Framework (https://osf.io/bvtxf/). For feasibility and greater comparability across studies, we limited our inclusion criteria to tasks focused on visual stimuli and upper-limb movements. A total of 110 articles were included in the review. Subtyping tasks were categorized based on whether they mainly manipulated aspects of the input (i.e., congruence of visual input with motor output, presence of visual input) or the output (i.e., modality, goal, or direction of output) to produce an Input-Output subtype dissociation. We used our review results to identify four main critiques of this literature: 1) lack of consistency/clarity in conceptual models; 2) methodological issues of dissociating Input and Output subtypes; 3) a need for updated neural theories; and 4) barriers to clinical application. We discuss the lessons learned from this subtyping dimension that can be applied to future research on neglect subtype assessment and treatment.
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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 | 0.198 | 0.496 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.048 | 0.043 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".