Pain, and Not Opioids, Is Associated With Delirium in Older Emergency Department Patients
Bibliographic record
Abstract
In Reply: We thank Dr. Meaden and collaborators for their interest in our study, their queries are interesting and deserve attention. It is true that the sample size for our cohort was previously calculated by Émond et al.1 to determine the incidence of delirium. However, the logistic regression analysis used in our study included the total 338 patients and not only the 41 patients with delirium. In addition, we reported a power calculation showing that with 338 patients, we had 80% chance of finding an odds ratio (OR) of at least 1.7 for a predictor of delirium, considering a baseline probability of 0.12 of delirium (41 cases). According to Chen et al.2 an OR of 1.7 is considered a small effect size by Cohen references, so we had sufficient power in our study to detect even a small effect size. It is also true that we did not have the power to examine the relationship between diagnoses and delirium in our study. However, in an ED setting, this relationship has not been clearly established. There was a significant association between patients who received opioids and patients with pain ≥ 65 (p ≤ 0.001), but not between opioids and delirium. Nevertheless, we agree with the comment that it was not possible with our data to know precisely how pain was managed during the study period. The level of pain was evaluated at the initial interview only and we do not know how the pain changes throughout the study, something that was acknowledged in the study’s limitations. We also agree that clinicians learn quickly that opioids cause delirium. However, this is based on low-quality data and maybe this teaching should change. The systematic review from Swart et al.,3 in 2017 cited by Dr. Meaden and colleagues to support this statement, reported that "there are some indications that meperidine and tramadol increase the risk of delirium […] The quality of existing research is limited." Moreover, pain was rarely controlled in the six studies included in the meta-analysis and the same authors state in the discussion section that "delirium as a result of pain, not as a result of opioid use, might play an important role in the included studies." Finally, they also state that “This review also suggests a protective effect of hydromorphone and fentanyl on delirium.”3 Our study provides more data to support the hypothesis that pain and not opioids is associated with delirium.
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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 | 0.009 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".