Bibliographic record
Abstract
Editor, We thank Drs Zilberman and Biro for their interest in our Editorial, and appreciate the opportunity to respond to their comments.1,2 The purpose of our Editorial was to point out where technology has failed us (delivered hypoxic guard systems) and describe how properly applied modern technology could improve overall safety in anaesthesia.2 Although Drs Zilberman and Biro mean well by describing a method to reduce the incidence of hypoxic inspired oxygen fraction (FIO2), it is not a foolproof system because it does not take into account the (remote) possibility that patient oxygen consumption is higher than expected. This could occur in situations such as thyroid storm or malignant hyperthermia. It also does not result in true low-flow anaesthesia, causing unnecessary expenses and pollution. Therefore, in our opinion, we should not continue to rely on ‘home-made regimens’ to prevent hypoxic FIO2; instead we should stimulate all of the anaesthesia machine manufacturers to use state-of-the-art technology to avoid and/or immediately correct such situations; this kind of technology is available today. There are several other issues with the statements by Zilberman and Biro that are based on serious misinterpretations. For example, the Brody formula should not be used in anaesthetised patients; it has been shown that the average oxygen consumption in an adult patient under anaesthesia is about 180 ml min−1, although there is a fairly wide range.3,4 Also, their proposed fresh gas flow of 800 ml min−1 is unnecessarily high, and modern technology allows us to (automatically) reduce agent consumption much more by using lower fresh gas flows. A very important issue is that they only consider the amount of oxygen delivered to the system and patient oxygen uptake, but they do not consider the amount of oxygen that leaves the system through the pop-off valve or ventilator relief valve; just because you add a certain amount of oxygen to the breathing system does not mean that all this oxygen is available to the patient. At the same time, the effects of rebreathing seem to be merely an afterthought. The rebreathed gas, at low flows, determines the final composition of inspired gas, much more than the delivered concentrations. Indeed, the authors fail to differentiate FDO2 (delivered oxygen fraction) from FIO2. These terms are not interchangeable! And finally, the authors use the term ‘low-flow ventilation’, which is really a misnomer. In conclusion, this letter confirms that we need sophisticated technology to help provide the safest possible conditions for our patients. Although we agree that teaching is important, education alone will not suffice. We hope that our reply further illustrates this point. There is no paradox: ‘sophisticated’ machines are needed to improve and maximize patient safety. Acknowledgements related to this article Assistance with the reply: none. Financial support and sponsorship: none. Conflicts of interest: JFAH has received lecture support, travel reimbursements, equipment loans, consulting fees and/or meeting organizational support of basically all companies involved with inhaled agent delivery (in alphabetical order): AbbVie, Acertys, Air Liquide, Allied Healthcare, Armstrong Medical, Baxter, Dräger, GE, Hospithera, Heinen und Lowensein, Intersurgical, Maquet, MDMS, MEDEC, Micropore, Molecular, NWS, Philips, Quantum Medical.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".