Herramientas de detección y seguimiento ambulatorio de desnutrición en pacientes oncológicos.
Notice bibliographique
Résumé
Introduction Cancer poses a significant global health and socioeconomic challenge. According to the World Health Organization (WHO), an estimated 40 % of cancer cases could be prevented by avoiding significant risk factors such as malnutrition. The prevalence of malnutrition in cancer patients is estimated to be between 30 % and 60 %. The multifactorial cause and development of malnutrition in cancer patients, coupled with the variety of tumors and different antineoplastic treatment options, can complicate adherence to treatment and result in a deterioration of patients’ quality of life. Oncology care is evolving towards a multidisciplinary model that incorporates a wide range of services and concerns, including monitoring the nutritional status of cancer patients. In this model, healthcare professionals play a crucial role in the early diagnosis or detection of malnutrition, the assessment of nutritional status, and a nutritional therapeutic approach. Approximately 90 % of cancer treatments and care are currently provided in outpatient settings, making these tasks even more vital in the management of cancer patients. Objective This report aims to assess the effectiveness, efficiency, and safety of tools for detecting and monitoring outpatient malnutrition in cancer patients, as well as the economic and organizational aspects and patients’ perspectives associated with implementing these tools in an outpatient setting. Methods A systematic review of the literature in two phases. The first phase limited the search to technology evaluation reports, systematic reviews and meta- analyses, followed by a second search to identify primary studies. Specific search strategies were developed, and the following electronic databases were consulted: Medline (Ovid), Embase (Excerpta Medica Database), Cochrane Library (Cochrane Review Database), INAHTA (International HTA Database), WOS (SCI Science Citation Index) and CINAHL (Cumulative Index of Nursing and Allied Literature). On the other hand, resources such as TripDataBase were consulted, as well as the leading websites of international agencies: National Institute for Health and Care Excellence (NICE), Canadian Agency for Drugs and Technologies in Health (CADTH), Agency for Healthcare Research and Quality (AHRQ) and the Spanish Network of Health Technology Assessment Agencies and Benefits of the SNS (RedETS). Finally, Clinical Trials Registers, ClinicalTrials.gov and the International Clinical Trials Registry Platform (ICTRP) were also consulted. Three independent researchers analysed car quality, and the synthesis of the results was carried out quantitatively. The tools selected to assess the quality of the included studies were AMSTAR-2 for systematic reviews and QUADAS-2 for primary diagnostic studies. Results Our systematic review included 35 primary studies in total. Of these, 30 evaluated variables related to diagnostic efficacy. Of the remaining 5 primary studies, two addressed organizational aspects, such as the need for nutrition training for professionals and patients, and the other three explored variables related to the patient’s perspective. Twenty-three tools were identified as index tests and 10 as comparators or reference standards. The tools analysed most frequently in the reviewed studies were MUST, MST, MNA, and PG-SGA (and its abbreviated version). The tools most commonly used as a reference method for concurrent validation were PG-SGA, GLIM and SGA in the selected studies. Conclusions The available evidence on the efficacy and safety of the tools identified in SRs and primary studies suggests that the detected tools may be suitable for identifying and diagnosing malnutrition in cancer patients in an outpatient setting. In general, integrating tools into the routine practice of outpatient clinics for detecting malnutrition and following up cancer patients may be helpful. However, it’s crucial to emphasize the necessity of an individual and continuous evaluation of their efficacy, safety, and cost-effectiveness for their implementation, ensuring ongoing improvement in patient care.
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 | 0,013 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».