Clinical Music Study Quality Assessment Scale (MUSIQUAS)
Bibliographic record
Abstract
AIMS Quality assessment of studies is essential for the understanding and application of these in systematic reviews and meta analyses, the two “gold standards” of medical sciences. Publications in scientific journals have extensively used assessment scales to address poor methodological quality, forming inclusion criteria or determine sensitivity of controls. Even though these assessments are commonplace in science publications, there is no scale, which assesses the quality of studies in the vast amount of music related sciences. METHODS Musiquas is based on the widely used Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analyses and was attuned by the authors to fit the demand of quality assessment in music studies and interventions. Initially 37 scoring points were included into the scale, distributed across the four main groups; Selection, Control criteria, Exposure and Outcome. These points were included from music studies addressing the importance of aspects in a methodological context and were compared against points used in the NOS excluding points showing methodological flaws against experimental studies as well as against the NOS. OUTCOMES The final scale assesses the quality of music studies and intervention on 26 points divided over the four main groups: Selection, Control criteria, Exposure and Outcome applying a 10-point rating. IMPLICATIONS Implications for sciences in music are obvious; from being able to assign more methodological value to a study to implications important for policy makers. Musiquas was published online, prior to this article, to make it available to researchers worldwide. This procedure gives insight into face and content validity of Musiquas, by receiving comments and critiques of fellow researchers. Evaluation of all remarks is currently in progress. Additionally, Musiquas was piloted in a systematic review on the relationship of music and the transfer effect
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.184 | 0.396 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".