Optimizing Neurobehavioral Assessment for Patients with Disorders of Consciousness: Proposal of a Comprehensive Pre-Assessment Checklist for Clinicians
Bibliographic record
Abstract
BACKGROUND: Clinicians are challenged by the ambiguity and uncertainty in assessing level of consciousness in individuals with disorder of consciousness (DoC). There are numerous challenges to valid and reliable neurobehavioral assessment and classification of DoC due to multiple environmental and patient-related biases including behavioral fluctuation and confounding or co-occurring medical conditions. Addressing these biases could impact accuracy of assessment and is an important aspect of the DoC assessment process. METHODS: A pre-assessment checklist was developed by a group of interdisciplinary DoC clinical experts and researchers based on the existing literature, current validated tools, and expert opinions. Once finalized, the checklist was electronically distributed to clinicians with a range of experience in neurobehavioral assessment with DoC. Respondents were asked to use the checklist prior to completing a neurobehavioral assessment. A survey was also provided to respondents to obtain feedback regarding checklist feasibility and utility in optimizing the behavioral assessments. RESULTS: Thirty-three clinicians completed the survey after using the checklist. Over half of the respondents were a combination of physicians, neuropsychologists, and physical therapists. All respondents served the adult DoC population and 42% percent had over ten years of clinical experience. Eighty percent reported they found the format of the checklist useful and easy to use. All respondents reported the checklist was relevant to preparing for behavioral assessment in the DoC population. Eighty-four percent reported they would recommend the use of the tool to other clinicians. CONCLUSIONS: The use of a pre-assessment checklist was found to be feasible and efficacious in increasing interdisciplinary clinician's ability to optimize the patient and environment in preparation for neurobehavioral assessment. Initial results of clinicians' perception of the utility of a pre-assessment checklist were positive. However, further validation of the tool is needed with larger sample sizes to improve representation of clinical use across disciplines and care settings.
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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 | 0.053 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".