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Record W4245692969 · doi:10.1097/aln.0000000000000169

In Reply

2014· letter· en· W4245692969 on OpenAlexaff
Michael Walsh, P.J. Devereaux, Daniel I. Sessler

Bibliographic record

VenueAnesthesiology · 2014
Typeletter
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicine

Abstract

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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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.042
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.066
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.005
Open science0.0030.003
Research integrity0.0180.032
Insufficient payload (model declined to judge)0.0420.034

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.

Opus teacher head0.020
GPT teacher head0.273
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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