Bibliographic record
Abstract
Dr Baggs has taken issue with several aspects of our recent publication on timing of transfer out of intensive care unit (ICU).1 First, her observation that we failed to identify the bedside nurse as part of the care team is a valid and appropriate criticism. In fact, we do believe that nurses are key members of the ICU team, and they were included in transfer decisions, when possible. We say “when possible” because in the ICU studied, the patient:nurse ratio was most commonly 2:1 or 3:1. As a result, only about half the time was a given patient’s own nurse able to be present in the morning rounds sessions during which such decisions were most frequently made. This information should have been included in the description of the ICU care team and the decision-making process for transfers.Dr Baggs also noted that none of our references related to the role of nurses in such transfer decisions. She identified 86 indexed references she found by cross-referencing search terms. However, ours was a quantitative study in which the topic was the affect of the timing of transfer on mortality, our addition of the term timing to her search resulted in just 5 publications, none of which were actually on the topic.Whereas involvement of nurses or others in decision-making might help avoid premature transfers (the initial descending slope of the curve in our article’s Figure), it seems quite unlikely that it would influence the harm we observed from patients whose transfer is delayed due to unavailability of regular ward beds. Nonetheless, we agree that studies of nurse participation in transfer decisions are germane to the issue, and it would have been reasonable to include them in the Discussion section of our article.Finally, referring to her own publications, Dr Baggs suggests that collaborative decision-making with nurses and other members of the care team might be superior to developing objective measures of readiness for transfer. Of course this is a testable hypothesis which, to-date, has not been tested. It seems most plausible to us, however, that both approaches have something to offer.
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 | 0.003 |
| 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 | 0.001 |
| 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 | 0.000 |
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".