An International and Multidisciplinary Consensus on the Labeling of Spatial Neglect Using a Modified Delphi Method
Bibliographic record
Abstract
Survivors of neurologic injury (most commonly stroke or traumatic brain injury) frequently experience a disorder in which contralesionally positioned objects or the contralesional features of individual objects are often left unattended or underappreciated. The disorder is known by >200 unique labels in the literature, which potentially causes confusion for patients and their families, complicates literature searches for researchers and clinicians, and promotes a fractionated conceptualization of the disorder. The objective of this Delphi was to determine if consensus (≥75% agreement) could be reached by an international and multidisciplinary panel of researchers and clinicians with expertise on the topic. To accomplish this aim, we used a modified Delphi method in which 66 researchers and/or clinicians with expertise on the topic completed at least 1 of 4 iterative rounds of surveys. Per the Delphi method, panelists were provided with results from each round prior to responding to the survey in the subsequent round with the explicit intention of achieving consensus. The panel ultimately reached consensus that the disorder should be consistently labeled spatial neglect . Based on the consensus reached by our expert panel, we recommend that researchers and clinicians use the label spatial neglect when describing the disorder in general and more specific labels pertaining to subtypes of the disorder when appropriate.
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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 | 0.276 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".