Validation of the GALS musculoskeletal screening exam for use in primary care: a pilot study
Bibliographic record
Abstract
BACKGROUND: As the proportion of the Canadian population > or =65 grows, so too does the prevalence of musculoskeletal (MSK) conditions. Approximately 20% of visits to family physicians occur as a result of MSK complaints. The GALS (Gait, Arms, Legs, and Spine) screening examination was developed to assist in the detection of MSK abnormalities. Although MSK exams are primarily performed by rheumatologists or other MSK specialists, expanding their use in primary health care may improve the detection of MSK conditions allowing for earlier treatment. The primary goal of this study was to evaluate the use of the GALS locomotor screen in primary care by comparing the results of assessments of family physicians with those of rheumatologists. The secondary goal was to examine the incidence of MSK disorders and assess the frequency with which new diagnoses not previously documented in patients' charts were identified. METHODS: Patients > or =65 years old recruited from an academic family health centre were examined by a rheumatologist and a family physician who recorded the appearance of each participant's gait and the appearance and movement of the arms, legs and spine by deeming them normal or abnormal. GALS scores were compared between physicians with the proportion of observed (Pobs), positive (Ppos) and negative (Pneg) agreement being the primary outcomes. Kappa statistics were also calculated. Descriptive statistics were used to describe the number of "new" diagnoses by comparing rheumatologists' findings with each patient's family practice chart. RESULTS: A total of 99 patients consented to participate (92 with previously diagnosed MSK conditions). Results showed reasonable agreement between family physicians and rheumatologists; Pobs = 0.698, Ppos = 0.614 and Pneg = 0.752. The coefficient of agreement (estimated Kappa) was 0.3675 for the composite GALS score. For individual components of the GALS exam, the highest agreement between family physicians and rheumatologists was in the assessment of gait and arm movement. CONCLUSION: Previously reported increases in undiagnosed signs and symptoms of musculoskeletal conditions have highlighted the need for a simple yet sensitive screening exam for the identification of musculoskeletal abnormalities. Results of this study suggest that family physicians can efficiently use the GALS examination in the assessment of populations with a high proportion of musculoskeletal issues.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".