Herramientas de detección y seguimiento ambulatorio de desnutrición en pacientes oncológicos.
Bibliographic record
Abstract
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.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".