VALIDITY OF THE VIRTUAL SEVERITY SCORING SYSTEM IN PATIENTS WITH KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
The Severity Scoring System (SSS) is a guide to interpreting findings across clinical, functional, and radiological findings, used by qualified, specially trained physiotherapists in the advanced practice role in order to provide consistency in determining the severity of the patient's condition and need for surgical consultation. The system has been utilized for over 14 years as a part of standardized assessment and management care and was incorporated into virtual care in 2020 following the pandemic restrictions. The present study examined the validity of the modified SSS in virtual care. Patients who were referred to the Rapid Access Clinic (RAC), were contacted via phone by two experienced advanced practice practitioners (APPs) from May to July 2020, when in-person care was halted due to the pandemic. The virtual interview included taking history, completing self-reported measures for pain and functional ability and reviewing the radiological reports. A total of 63 patients were interviewed (mean age 68, SD=9), 34 (54%) females. Of 63 patients, 33 (52%) were considered a candidate for total knee arthroplasty (TKA). Men and women were comparable in age, P4 and LEFS scores. The TKA candidates had a significantly higher SSS (p<0.0001) and pain scores (p=0.024). The variability of the total SSS score explained by the functional, clinical and radiological components of the tool were 55%, 48% and 4% respectively, highlighting the more important role of patient's clinical history and disability in the total SSS. The virtual SSS is a valid tool in directing patients for surgical management when used by highly trained advanced practice physiotherapists. A large component of the SSS is based on clinical data and patient disability and the APP's skillset rather than severity of pathology found on imaging.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".