An Audit about Music Therapy Assessments and Recommendations for Adult Patients Suspected to be in a Low, Awareness State
Bibliographic record
Abstract
In neuro-rehabilitation, the role of music therapy is expanding to include assessment of patients with severely-altered states of consciousness. Diagnosis of these conditions is a complex task for all, and cases of misdiagnosis have been reported. Aggregated findings from 33 music therapy assessments of patients suspected of being in a low awareness state are described and discussed here. The Music Therapy Assessment Tool for Low Awareness States (MATLAS) was used during these assessments. All assessments were offered as part of a specialist multidisciplinary assessment package. A brief description of the patient group is supplied, along with details regarding the assessment tool and the recommendations that followed. In summary, a difference in the time it took to assess patients in vegetative state (VS) as compared to those in minimally conscious state (MCS) was found and, on average, the assessment of those in VS took less time to complete than for those in MCS. A greater range in session length was found for patients in VS, as compared to those in MCS. Generally after the assessments, patients in VS were likely to be admitted to a sensory regulation group administered by a music therapy assistant, supervised by a qualified music therapist, to enable the continued collection of behavioral responses to stimuli. Patients in MCS were admitted to a music therapy treatment program offered by a qualified music therapist. Ongoing work is recommended to advance the assessment and treatment of this patient population, and to consolidate the role of music therapy with this population.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".