Bibliographic record
Abstract
We wish to thank Dr Kawada for the letter regarding our article “Sleep-disordered breathing, postoperative delirium, and cognitive impairment.”1 Dr Kawada raised several salient points regarding our review. Preexisting cognitive impairment is a risk factor for developing postoperative delirium, and patients with cognitive impairment associated with sleep-disordered breathing (SDB) may be at higher risk for developing postoperative delirium (POD). The aim of our review was to present the evidence supporting a possible relationship between SDB, cognitive impairment, and POD. We discussed possible pathophysiological mechanisms for an association between SDB and POD, and focused on considerations relevant to surgical patients specifically in the perioperative period. It was not our intention to do a meta-analysis of the studies on SDB and cognitive impairment. We agree with Dr Kawada that the meta-analysis conducted by Leng et al2 helps to clarify the controversy about the relationship between SDB and cognitive impairment. Leng et al2 reported that a pooled analysis of 6 prospective studies showed people with SDB were 26% more likely to develop cognitive impairment. This finding strengthens our hypothesis that cognitive impairment may be a link between SDB and POD. We acknowledge and agree that the confounding factors should be analyzed with a statistical model to select for independent variables between SDB and cognitive impairment. Kerner and Roose’s3 study presented a model of key pathophysiological mechanisms with special reference to the cerebral microvascular and neurovascular system. Their findings highlight another factor that could influence the complex pathophysiological pathway for POD through SDB. Finally, Vaessen et al4 did not specifically investigate the effect of continuous positive airway pressure (CPAP) on the improvement of cognitive impairment and they did not state CPAP as a search term for their literature search. Only 2 articles discussed by Vaessen et al4 mentioned CPAP and both did not perform cognitive tests; Mulgrew et al5 studied the effect of CPAP on work-related factors such as time management, and Vernett et al6 measured the effect of CPAP on residual excessive sleepiness. Our review of the literature specifically included studies that investigated the effect of CPAP on cognitive functioning in elderly patients with SDB to show the potential of using CPAP to reduce the risk of POD. Due to the limited studies that have linked SDB with POD and the relatively unexplored nature of the topic, we wished to raise awareness among anesthesiologists and other perioperative physicians. We hope more physicians will consider undiagnosed SDB in their differential diagnosis for patients with POD because SDB is highly prevalent, has complicated physiological effects, and may be a treatable cause contributing to POD. Because POD is a serious complication after surgery and there are few treatments for POD, future studies are needed to evaluate the relationship between SDB, cognitive impairment, POD, and the efficacy of CPAP treatment. Enoch W. K. Lam, BHSc StudentFrances Chung, MBBS, FRCPCJean Wong, MD, FRCPCDepartment of AnesthesiaToronto Western HospitalUniversity Health NetworkUniversity of TorontoToronto, Ontario, Canada.[email protected]
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 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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.210 | 0.122 |
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".