Minimal clinical datasets for spine-related musculoskeletal disorders in primary and outpatient care settings: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Effective measurement and monitoring of health status in patients with spine-related musculoskeletal (MSK) disorders are essential for providing appropriate care and improving outcomes. Minimal clinical datasets are standardized sets of key data elements and patient-centered outcomes that can be measured and recorded during routine clinical care. Our scoping review aimed to identify and map current evidence on minimal clinical datasets for measuring and monitoring health status in patients with spine-related MSK disorders in primary and outpatient healthcare settings. STUDY DESIGN AND SETTING: We followed the JBI (formerly Joanna Briggs Institute) methodology for scoping reviews. MEDLINE, CINAHL, Cochrane Library, Index to Chiropractic Literature, MANTIS, ProQuest Dissertations and Theses Global, and medRxiv preprint repository were searched from database inception to August 1, 2021. Two reviewers independently screened titles and abstracts, full-text articles, and charted the evidence. Findings were synthesized and summarized descriptively. RESULTS: After screening 5,583 citations and 301 full-text articles, 104 studies about 32 individual minimal clinical datasets were included. Most minimal clinical datasets were developed for patient populations with spine-involving inflammatory arthritis, nonspecific or degenerative spinal pain, and MSK disorders in general. The minimal clinical datasets varied substantially in terms of the author-reported time-to-complete (1-48 minutes) and the number of items (5-100 items). Fifty percent of the datasets involved healthcare professionals in their development process, and only 28% involved patients. Health domain items were most frequently linked to the components of activities and participation (43.9%) and body functions (28.6%), according to the International Classification of Functioning, Disability, and Health. There is no standardized definition of minimal clinical datasets to measure and monitor health status of patients with spine-related MSK disorders in routine clinical practice. Common core elements identified were practicality, feasibility in a busy routine practice, time efficiency, and the capability to be used across different healthcare settings. CONCLUSION: Due to the absence of a standard definition for minimal clinical datasets for patients with spine-related MSK disorders, there is a lack of consistency in the selection of key data elements and patient-centered outcomes that should be included. More research on the implementation and feasibility of minimal clinical datasets in routine care settings is warranted and needed. It is essential to involve all relevant partners in the development process of minimal clinical datasets to ensure successful implementation and adoption in routine primary care.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.061 | 0.283 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.048 | 0.043 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".