Validation of the four‐dimensional symptom questionnaire (4DSQ) and prevalence of psychological symptoms in orthopedic shoulder patients
Bibliographic record
Abstract
Psychological problems are common in shoulder patients. A validated psychological questionnaire measuring clinically relevant psychological symptoms (including distress, depression, anxiety, and somatization) in shoulder patients is lacking. The Four-Dimensional Symptom Questionnaire (4DSQ) is a self-report questionnaire to identify distress, depression, anxiety, and somatization which has been validated in primary care populations. The aim of this study was to validate the 4DSQ in orthopedic shoulder patients. We assessed whether the 4DSQ measures these four constructs the same way in an orthopedic population with shoulder problems compared to a general practice population. We also investigated the prevalence of psychological symptoms in shoulder patients. The shoulder group consisted of 200 consecutive patients and the general practice group comprised 368 patients, matched for gender and age. Differential item functioning analysis showed that the 4DSQ measures the different psychological symptoms in orthopedic shoulder patients the same way as in general practice patients. The shoulder patients tended to score higher on the somatization scale, resulting in a new cut-off point for somatization. The prevalence of distress, somatization, anxiety, and depression in the shoulder group was 23%, 14%, 10%, and 8%, respectively. It can be concluded from this study that the 4DSQ in orthopedic shoulder patients measures the same constructs as in general practice patients and can therefore be used in orthopedic practice to measure psychological symptoms in patients with shoulder complaints.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".