Decision Support Systems in Anesthesia, Emergency Medicine and Intensive Care Medicine
Bibliographic record
Abstract
Decision support systems (DSS) in the context of anesthesia, critical care and intensive care medicine consist of several components: the ‘brain’, which integrates a variety of input parameters and delivers as output various informative data which can help the physicians to perform better. They might be considered as ‘automated textbooks’ which by the use of a digitalized infrastructure not only deliver immediately all the necessary health care knowledge to the clinician but also has the capability of intelligently monitoring the actual health status of any given patient – ‘smart monitoring’. It is obvious that in the context of anesthesia, critical – emergency – care and intensive care medicine, such systems are a vital backbone of medical care of the 21st century with a growing number of parameters, growing number of diseases, diagnostic possibilities and treatment options. Decision support systems have the potential to bridge the gap between the theoretical performance of a well trained physicians –who has spent a decade acquiring knowledge and a multitude of manual skills – and his or her actual performance in daily practice. This latter performance can be influenced by any given contextual condition, be it emotional, intellectual or behavioral pattern. What we define as ‘clinical error’ is influenced by several mistakes, one of the most prominent the impossibility to recall all diagnostic and therapeutic options any time for any patient – ‘prospective recall failure’. Computerized information tools have been investigated for more than 2 decades and have been proven to be highly effective in a research environment. However, especially in the specialties which are the object of this chapter, they have not been widely introduced into practice. One of the problems is the clustering of information in modern healthcare facilities between hospital administrators, several laboratory or investigative units and health care providers. The complexity of modern diagnostic and therapeutic options is such that only the most complete coverage of all available patient information can deliver a most complete decision support for the clinician. A lack of standardization, immediate delivery of patient data due to inherent lag times of different working systems, make some of these systems inefficient in reality. Another problem is the ‘user-friendliness’ of these systems – the significant additional time necessary to ‘feed’ the data into the system, as well as the gradual integration of these decision support systems into the daily work pattern: accessibility of the
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".