Total Hip Arthroplasty in A Young Patient with Bernard-Soulier Syndrome.
Bibliographic record
Abstract
INTRODUCTION: The management of patients with coagulopathic disorders undergoing orthopaedic surgery requires a dedicated, multi-disciplinary team with detailed perioperative planning. Bernard-Soulier Syndrome (BSS) is an extremely rare disorder, affecting 1 in 1 million individuals worldwide. It is caused by a deficiency in glycoprotein 1b-V-IX which is required for normal platelet-mediated clot formation. The deficiency results in prolonged bleeding time with high risk of spontaneous bleeds. Few reports exist in the clinical literature of BSS patients undergoing major surgery. CASE REPORT: A 40 year old, female with known BSS and developmental dysplasia of her left hip (DDH) was referred to us for consideration of left total hip arthroplasty (THA). Consultation with her Haematologist for pre-operative optimization of platelets and related clotting times together with detailed discussions of her intended anaesthesia protocol and surgery resulted in a successful operation with less than anticipated blood loss. She entered our rehabilitation program just one week after surgery. CONCLUSION: BSS is an extremely rare bleeding disorder that puts patients at very high risk of blood loss following surgery. This is the first report that we are aware of describing a BSS patient undergoing a THA. A cohesive, highly specialized, multi-disciplinary team is crucial to the success of these patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".