The Virtual Shoulder and Knee Physical Examination
Bibliographic record
Abstract
The COVID-19 crisis has forced a sudden and dramatic shift in the way that clinicians interact with their patients, from outpatient encounters to telehealth visits utilizing a variety of internet-based videoconferencing applications. Although many aspects of pre-COVID-19 outpatient sports medicine care will ultimately resume, it is likely that telehealth will persist because of its practicality and because of patient demand for access to efficient and convenient health care. Physical examination is widely considered a critical obstacle to a thorough evaluation of sports medicine patients during telehealth visits. However, a closer reflection suggests that a majority of the examination maneuvers are possible virtually with limited, if any, modifications. Thus, we provide a comprehensive shoulder and knee physical examination for sports medicine telehealth visits, including (1) verbal instructions in layman's terms that can be provided to the patient before or read verbatim during the visit, (2) multimedia options (narrated videos and annotated presentations) of the shoulder and knee examination that can be provided to patients via screen-share options, and (3) a corresponding checklist to aid in documentation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".