Perioperative Mortality Secondary to a Pulmonary Embolism During a Surgical Implantation of the Endo-Exo-Prosthesis: A Case Report
Bibliographic record
Abstract
We report on a 53-year-old female patient who suffered a perioperative death secondary to a pulmonary embolism (PE) during an implantation of Endo-Exo-Prosthesis. This is a retrospective review of medical case for a patient who had a previous above-the-knee amputation secondary to a failed previous arthroplasty surgery. Our planned surgery was a stage 1 implantation of an Endo-Exo-Prosthesis, and it was performed under general anaesthesia. After 25 min from starting the surgical procedure, the patient sustained a cardiac arrest, and despite an active cardiopulmonary resuscitation for 50 min, the patient did not recover, the ventilator machine was stopped later on, and the patient was declared deceased at that stage. Fatal intraoperative PE is a rare but significant complication during orthopaedic procedures. There are few reports of similar events but include mainly trauma patients with fractured neck of femur. Endo-Exo-Prosthesis is a relatively newly evolved procedure in a unique group of patients. To our knowledge, this is the first case report of such complication during Endo-Exo-Prosthesis implantation. Patient and surgeon should be aware of it, and additional preventive measures like preoperative scoring systems and in special cases using inferior vena cava filter should be considered in patients with high risk of developing venous thromboembolism.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".