Notice bibliographique
Résumé
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]
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,013 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,210 | 0,122 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».