How we treat: considerations for physiotherapy in the patient with haemophilia and inhibitors undergoing elective orthopaedic surgery
Bibliographic record
Abstract
Ten weeks prior to a scheduled left total knee arthroplasty, a 25‐year‐old man with severe haemophilia A and a high‐titre inhibitor presented to the physical therapist for a preoperative assessment at the haemophilia treatment centre (HTC). Prior to the recommendation to proceed to surgery, the therapist and other members of the multidisciplinary care team had surmised that, despite two previous radiosynovectomies, an arthroscopic synovectomy, and most recently at the age of 18 years, an arthroscopic debridement, the patient continued to have a progression of joint disease manifested by pain, restricted range of motion and reduced strength. Based on these findings and the patient’s history of consistent adherence to and follow‐through with recommended treatments, the HTC staff and orthopaedic surgeon determined that he was a good candidate for total knee replacement. The patient’s pain and joint disease severely limited his mobility and participation in functional activities. The most recent radiographs were notable for severe tricompartmental arthropathy of the left knee with joint deformity, flexion contracture, and osteoporosis. When queried about current haemostatic therapy during the initial preoperative visit with the physical therapist, the patient explained that he was self‐infusing a bypassing agent to treat active bleeds and as prophylactic treatment before participating in vigorous physical activity, as advised by his haematologist. He had previously managed his joint pain with cyclooxygenase‐2 inhibitors and both short‐ and long‐acting opioids, in addition to physical therapy. His current personal inventory of mobility and rehabilitative aids consisted of crutches, compressive wraps, and a cold‐compression unit. Further assessment of relevant environmental and psychosocial factors revealed that the patient was living with his girlfriend and 3‐year‐old daughter, for whom he was the primary caregiver, in a two‐story home with five steps to enter. He was also working part‐time from home as a computer consultant. The visit concluded with a formal physical assessment and discussion of next steps, including plans for additional preoperative physical therapy sessions. The therapist also informed the patient about what to expect postoperatively in terms of rehabilitation and recovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".