Bibliographic record
Abstract
Xue et al. point out that not all potential variables were considered in our analyses and suggest that body mass index and ethnicity may confound the association between hypotension and organ injury. Revised models that include these two variables demonstrate no important effect on the relationship between hypotension and our outcomes (table 1).Xue et al. also wonder whether organ injury results from the hypotension or its treatment. As we discussed in our article, this certainly needs to be considered when interpreting our results.1 However, including vasopressor use in our analyses is unlikely to be helpful. The issue of indication bias (i.e., the severity of hypotension is correlated to the likelihood of receiving vasopressors and the dose of vasopressor received) is extremely difficult to resolve even with advance statistical techniques. Randomized, controlled trials to prevent hypotension are likely the only sufficiently robust method of determining whether a mean arterial pressure less than 55 mmHg is injurious. Importantly, our work informs the definition of hypotension for any such trial and thereby improves the likelihood treatment can be studied safely and with maximum likelihood of demonstrating a benefit.Xue et al. also question the timing of creatinine and cardiac enzyme determinations in relation to the time of surgery. We defined the outcome as within 7 days of surgery, and our sensitivity analyses restricting the definition to within 3 days of surgery demonstrated no material differences. We agree that early detection of organ injury, particularly in the first 3 days after surgery, is a crucial first step in discovering effective treatments for perioperative events. The Vascular events In noncardiac Surgery patIents cOhort evaluatioN (VISION) study demonstrated that more than half of myocardial injuries would be missed without routine postoperative troponin monitoring during the initial 3 postoperative days.2 Older studies suggest that more than 80% of acute kidney injury, which is usually clinically silent, also typically occurs in a similar time frame and this is corroborated by our study in which 82% of acute kidney injury occurred in the first 3 days after surgery.3 Further researches to establish effective treatments after perioperative complications such as renal and cardiac injury are urgently needed.Xue et al.’s final comment suggests that we need to determine the cause of death and its relatedness to organ injury. This information is not available in our dataset; furthermore, we do not believe that relatedness can be reliably determined or likely to be immediately helpful. Take for example the following hypothetical scenario. A patient suffers a silent intraoperative myocardial injury that results in postoperative delirium and fatigue and mild acute kidney injury. This results in immobility and ultimately in a venous thromboembolism. The ensuing chest pain is treated with narcotics which along with the some mild thrombotic event results in some pulmonary function compromise. This predisposes the patient to a hospital-acquired pneumonia which is ultimately fatal. Had any one of these factors been avoided, the patient may have survived. However, attributing the cause of death to any single complication of surgery ignores the contributions of the others. Because of the complicated nature of the causal pathways to postoperative death, we do not believe that it is appropriate to examine specific causes of death in analyses such as ours.The authors declare no competing interests.
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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 | 0.005 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.042 | 0.034 |
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".