Development and application of clinical prediction rules to improve decision making in physical therapist practice
Bibliographic record
Abstract
linical 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 [Childs JD, Cleland JA. Development and application of clinical prediction rules to improve decision making in physical therapist practice. Phys Ther. 2006;86:122–131.]
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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 | 0.026 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".