Case Report: Schizophrenia Discovered during the Patient Interview in a Man with Shoulder Pain Referred for Physical Therapy
Bibliographic record
Abstract
PURPOSE: The purpose of this case report is to demonstrate the importance of a thorough patient interview. The case involves a man referred for physical therapy for a musculoskeletal dysfunction; during the patient interview, a psychiatric disorder was recognized that was later identified as schizophrenia. A secondary purpose is to educate physical therapists on the recognizable signs and symptoms of schizophrenia.Client description: A 19-year-old male patient with chronic shoulder, elbow, and wrist pain was referred for physical therapy. During the interview, the patient reported that he was receiving signals from an electronic device implanted in his body.Measures and outcome: The physical therapist's initial assessment identified a disorder requiring medical referral. Further management of the patient's musculoskeletal dysfunction was not appropriate at this time. INTERVENTION: The patient was referred for further medical investigation, as he was demonstrating signs suggestive of a psychiatric disorder. The patient was diagnosed with schizophrenia by a psychiatrist and was prescribed Risperdal. IMPLICATIONS: This case study reinforces the importance of a thorough patient interview by physical therapists to rule out non-musculoskeletal disorders. Patients seeking neuromusculoskeletal assessment and treatment may have undiagnosed primary or secondary psychiatric disorders that require recognition by physical therapists and possible medical referral.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".