Clinical practice guideline of the spanish society of oral surgery for oral surgery in patients with coagulation disorders
Bibliographic record
Abstract
BACKGROUND: The number of patients treated with coagulation disorders, and more specifically with anticoagulant therapy, has increased worldwide in recent years due to increased life expectancy in developed countries. The protocols for managing this type of patient in oral surgery has varied over recent years, especially after the appearance of new direct-acting oral anticoagulants (DOACs). The assessment of risk of bleeding in this type of patient when undergoing a surgical procedure continues to be a controversial issue for patients, dentists and general practitioners. The objective of this document is to offer recommendations, based on evidence, for decision making for patients with coagulopathies who require dental surgical intervention. MATERIAL AND METHODS: Based on the indications of the "Preparation of Clinical Practice guidelines in the National Health System. Methodological manual", we gathered a group of experts who agreed on 15 PICO questions based on managing patients with coagulation disorders in dental surgical procedures, such as fitting of implants or dental extractions. RESULTS: The 15 PICO questions were answered based on the available evidence, being limited in most cases due to the lack of a control group. Two of the PICO questions were answered by the experts with a grade C recommendation, while the rest were answered with grade D. CONCLUSIONS: The results of this review highlight the need to undertake well designed clinical trials with control groups and with a representative sample size.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".