Management of antithrombotic therapy during cardiac implantable device surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anticoagulants are commonly used drugs that are frequently encountered during device placement. Deciding when to halt or continue the use of anticoagulants is a balance between the risks of thromboembolism versus bleeding. Patients taking warfarin with a high risk of thromboembolism should continue to take their warfarin without interruption during device placement while ensuring their international normalized ratio remains below 3. For patients who are taking warfarin and have low risk of thromboembolism, either interrupted or continued warfarin may be used, with no evidence to clearly support either strategy. There is little evidence to support continuing direct acting oral anticoagulants (DOACs) for device implantation. The timing of halting these medications depends largely on renal function. If bleeding occurs, warfarin׳s anticoagulation effect is reversible with vitamin K and activated prothrombin complex concentrate. There are no DOAC reversal agents currently available, but some are under development. Regarding antiplatelet agents, aspirin alone can be safely continued while clopidogrel alone may also be continued, but with a slightly higher bleeding risk. Dual antiplatelet therapy for bare-metal stent/drug-eluting stent implanted within 4 weeks/6 months, respectively, should be continued due to high risk of stent thrombosis; however, if they are implanted after this period, then clopidogrel can be halted 5 days before the procedure and resumed soon after, while aspirin is continued. If the patient is taking both aspirin and warfarin, aspirin should be halted 5 days prior to the procedure, while warfarin is continued.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| 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 it