Development and Application of Clinical Prediction Rules to Improve Decision Making in Physical Therapist Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinical prediction rules (CPRs) are tools designed to improve decision making in clinical practice by assisting practitioners in making a particular diagnosis, establishing a prognosis, or matching patients to optimal interventions based on a parsimonious subset of predictor variables from the history and physical examination.1,2 Clinical prediction rules have been developed to improve decision making for many conditions in medical practice, including the diagnosis of proximal deep vein thrombosis (DVT),3 strep throat,4 coronary artery disease,5 and pulmonary embolism.6 Clinical prediction rules also have been developed to assist in establishing a prognosis such as determining when to discontinue resuscitative efforts after cardiac arrest in the hospital,7 determining the likelihood of death within 4 years for people with coronary artery disease,7 identifying children who are at risk for developing urinary tract infections,8 and identifying the characteristics of patients who are likely to develop postoperative nausea and vomiting after anesthesia.9 Clinical prediction rules have recently been developed that can improve decision making in physical therapist practice. Examples include prediction rules to improve the accuracy of diagnosing ankle fractures (ie, “the Ottawa Ankle Rules”)10 and knee fractures (ie, “the Ottawa Knee Rules”)11 in people with acute injuries and to determine when to order radiographs in patients with neck trauma.12 Other prediction rules have been developed to diagnose patients with cervical radiculopathy13 and carpal tunnel syndrome.14 A CPR also has been developed to establish the prognosis of patients with neck pain following a rear-end motor vehicle accident.15 With increasing attention focused on the rising costs of health care, CPRs provide practitioners with powerful diagnostic information from the history and physical examination that may serve as an accurate decision-making surrogate for more expensive diagnostic tests. For example, the Ottawa …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it