Bibliographic record
Abstract
I t was a pleasure to read the editorial by Dr. Hemmerling, and it was very promising to see open discussion about technological innovation affecting the field. 1 I agree that automated robotic systems appear inevitable.However, I would wish to comment that it is important to highlight both artificial intelligence and robotics as two distinct innovations that work synergistically together in this context.In a future where robots are responsible for delivery of anesthesia in theater, both innovations will require substantial development to ensure that the system can adequately learn from and respond to variation.Assuming any rate of improvement to these systems, they may soon begin to outperform humans, and the input of the human anesthetist may gradually shift toward a supervisory role.One of the main advantages of machine learning algorithms is that they are capable of outperforming human decision-making, provided that their datasets are reliable and their decision-making has been refined appropriately before use.Dr. Hemmerling illustrates a good example of what anesthesiology in theater may look like in 2030, assisted by a robot.In this example, it is the human who offers the instructions and the robot executes them.This contrasts with other specialties, such as radiology and dermatology, where deductive artificial intelligence systems may offer diagnoses for the human to confirm and then act upon.If artificial intelligence systems are trained on large, diverse, and clean datasets, they should, in theory, be able to make decisions on the type of anesthesia to be performed and the various target parameters.Moravec's paradox dictates that programming artificial intelligence systems to complete these complex cognitive tasks is often relatively straightforward when compared to simple physical robotic tasks. 2 Before we see robots take over the delivery of anesthesia, they may begin to take over the instruction of what to deliver.Dr. Hemmerling compares the technology to the emergence of self-driving cars, which require similar alliance between artificial intelligence and robotics.This is a particularly appropriate comparison considering that issues of data homogenization, accountability, and unrepresentative datasets must be resolved before both of these technologies populate our highways and operating theaters.The timescales are uncertain, but that does not make them impossible.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.063 | 0.100 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".