Identification of a Fibular Fracture in an Intercollegiate Football Player in a Physical Therapy Setting
Bibliographic record
Abstract
Injuries to the ankle or foot are some of the most common orthopaedic complaints seen in primary care and sports medicine settings, accounting for 5% to 10% of all visits. Physical therapists working in a military setting are frequently the first credentialed providers to evaluate and diagnose patients with musculoskeletal complaints or orthopaedic trauma, using their privileges to order radiographs, bone scans, and electromyographical/nerve conduction study examinations. Because the presenting symptoms of sprains and fractures are often similar, it is imperative that physical therapists are competent and comfortable with their role of evaluating acute traumatic injuries without a physician referral. The validity of physical therapists managing patients with acute musculoskeletal injuries, without physician referral, has been previously established. This important role has enabled US Army orthopaedic surgeons to focus their practice on more complicated trauma or surgical cases. As direct access becomes more prevalent in the civilian profession of physical therapy, it becomes increasingly important that the physical therapist, as the first credentialed provider evaluating the patient, is proficient in distinguishing between ankle sprains and fractures. Even in the absence of direct access, physical therapists should still be able to determine when radiographs are appropriate in the event of a misdiagnosis and referral for an ankle sprain. The Ottawa Ankle Rules and the Buffalo modification are effective clinical decision rules to assist therapists in ruling out a fracture or determining whether radiographs are necessary for acute ankle injuries. We chose to report this case as example of how physical therapists can effectively apply these rules while serving in a direct-access role for the benefit of patients. J Orthop Sports Phys Ther. 2004;34(4):182–186. doi:10.2519/jospt.2004.1310
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".