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Enregistrement W3198115169

Screening Tools for Chronic Post-Surgical Pain [Internet]

2021· article· en· W3198115169 sur OpenAlexaboutno aff
Charlotte Wells, Suzanne McCormack

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineMedicine
ThématiqueAnesthesia and Pain Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineChronic painQuality of life (healthcare)OpioidPsychological interventionIncidence (geometry)Physical therapyInternal medicinePsychiatryNursing
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Chronic post-operative pain (or chronic post-surgical pain [CPSP]) is a potential long-term complication of surgical interventions. CPSP is generally defined as pain that develops after a surgical intervention, lasts greater than 2 months, interferes with quality of life, is a continuation of pain developed in the acute phase post-surgery, or develops after a period with no pain, localized to the surgical area, and is not caused by other factors. Prolonged post-operative pain can lead to high health care utilization and costs, poorer clinical outcomes for patients, and lower quality of life. Depending on which surgery is performed, the incidence of lasting chronic pain can be from 5% to 85%. In Ontario, chronic pain (in general) costs CA$1,742 per person in 2014 (approximately CA$10 billion per year). As CPSP is a common driver of the rates of chronic pain, CPSP contributes to a large portion of health care spending in Ontario, not including a patient’s direct or indirect out-of-pocket costs. Additionally, the development of CPSP is linked to higher rates of opioid consumption. Persistent opioid use is associated with higher mortality and morbidity, and patients taking opioids to help with CPSP still report moderate to severe pain, higher disability, and lower overall global health. As opioid use and misuse is a global epidemic, strategies to reduce the development of CPSP may lower the use of opioids for pain relief.Risk factors for the development of CPSP can include pain before and after the operation (and severity of that pain), the type of surgery being performed, posttraumatic negative affect, and pain catastrophizing (i.e., exaggeration of a negative mental health state). In 1 systematic review examining factors related to the development of CPSP, preoperative factors that showed significant correlation with pain development were younger age (excluding pediatric patients), female sex, smoking, history of depressive or anxiety symptoms, sleep difficulties, higher body mass index, preoperative pain, and use of preoperative analgesia. Other suggested factors included genetics, length of surgery, and surgical techniques (e.g., type of surgical method, amount of trauma to area, and amount of tissue handling).,It has been proposed that tools that are used to measure these factors may be applicable in both predicting patients who may develop chronic pain and in the prevention of chronic pain. Tools that can classify patients as a higher risk may help physicians tailor both treatment and preventive measures for these patients or may prompt them to provide more intensive care.Validated tools to assess risk factors associated with CPSP include psychological assessments (e.g., Hospital Anxiety and Depression Scale, Pain Anxiety Symptoms Scale, Beck Depression Inventory, Amsterdam Preoperative Anxiety and Information Scale), pain catastrophizing (e.g., pain catastrophizing scale12), pain assessments (e.g., 6-factor risk model for CPSP13), and quality of life assessments (e.g., EuroQuol 5-Dimensions Questionnaire14). However, some models used to predict chronic pain can be narrow in that they do not include multiple surgical factors (e.g., laparoscopy versus open surgery) that can contribute to CPSP; specifically, they may not provide consistent definitions of psychosocial factors leading to CPSP and may not be fully validated in certain populations. Additionally, these models may be underpowered (lacking large datasets required for accuracy) and do not identify all the factors relevant to CPSP. With the goal of using validated tools for predicting the development of CPSP, it has been suggested that, if a patient is identified to be at high risk of developing CPSP, preventive measures could be taken. Some potential measures include providing preoperative analgesia, providing selective norepinephrine and serotonin reuptake inhibitors, using laparoscopic or less invasive surgery when possible, using regional anesthesia, and providing other pain-minimizing pharmaceuticals such as IV lidocaine, ketamine, or glucocorticoids.The objective of this report is to summarize evidence regarding the clinical utility of perioperative screening or prediction tools for preventing CPSP.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,029
Score d'incertitude au seuil0,092

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,014
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0270,006

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,285
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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