PRECISION MEDICINE AND ANAESTHESIA: CURRENT CLINICAL AND GENOMICS APPROACHES.
Bibliographic record
Abstract
The field of anaesthesia, or anesthesiology, has undergone several advancements in recent years, becoming more precise and personalized to patients' needs, and more viable in clinical settings. As a result, anesthesiology has become more viable in clinical settings, tailored to patients' needs. Furthermore, it is not only the intraoperative care during surgery that requires attention, but also the pre-operative and post-operative care, as any coexisting conditions can obstruct proper pain management and recovery, and also to focus on any preexisting comorbidities to provide a more personalised anaesthesia care. And when it comes to a personalised anaesthesia plan, the patient's genomic data plays a crucial role, as it can enhance not only the effectiveness of the anaesthetics used but also the safety and reliability of total anaesthesia care. Therefore, pharmacogenomics is a critical factor in the evolution of anesthesiology and, furthermore, also plays a pivotal role in precision medicine. In this review, we revise Current Clinical and genomics approaches regarding anaesthesia. Taken all together, the field of anaesthesia is increasingly dependent on precision and personalised approaches, thereby significantly improving patient safety. A greater focus on personalised approaches can be achieved by fully incorporating pharmacogenomics; it is precisely based on the patient's genetic characteristics that individualized treatment plans can be developed. Identifying specific genetic markers that affect drug metabolism and efficacy will enable clinicians to enhance the effectiveness and safety of anaesthetic care. Accordingly, the transition from a generic approach to a more tailored strategy significantly reduces the risk of adverse drug reactions, while also contributing to and focusing on better therapeutic outcomes. Overall, it contributes to patient recovery and a better prognosis. Pharmacogenomics, nanotechnology, three-dimensional printing technology, and AI hold potential to transform precision anaesthesia, enhancing perioperative care by providing patient-centered drug delivery systems, customized surgical tools, and improved therapeutic outcomes personalised to patients' needs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".