Avaliação do impacto nutricional do tratamento antineoplásico de pacientes com câncer de cabeça e pescoço de interesse do cirurgião-dentista: revisão sistemática
Bibliographic record
Abstract
2022.Versão Corrigida.Head and neck squamous cell cancer is increasing worldwide, and its treatment still brings severe oral complications, quality of life negative impact and nutritional status of these patients.Post-oncotherapy malnutrition can often be related to deleterious symptoms that arise along the treatment.The maxillofacial Prosthodontist has knowledge to work together with the multidisciplinary team in order to reduce these side effects and rehabilitation of these patients.This systematic review aims to evaluate the post-treatment symptoms of head and neck cancer, which may impact the nutritional status which are of interest to the oral and maxillofacial prosthodontist for the rehabilitation of these patients.A systematic electronic search was performed in Pubmed, Embase (via Elsevier), Web of Science, Scopus, Lilacs (via BVS) and Cochrane Library databases, from 2011 to 2022, which resulted in a 1853 articles initial sample.The Joanna Briggs Institute tools for cross-sectional studies and the Newcastle Ottawa Scale were used to assess the risk of bias.13 observational studies were selected for qualitative synthesis.Of all studies, 5 received a low risk of bias classification, 7 studies a moderate risk of bias and 1 study a high risk of bias.The swallowing, weight loss, xerostomia and masticatory function outcomes showed a low certainty of evidence, and the trismus outcome had very low certainty of evidence.Among the main symptoms evaluated, swallowing was presented in 11 articles, while 4 articles evaluated trismus.The scientific content collected showed great heterogeneity between the studies, and the nutritional symptons impact status are always present after oncotherapy.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".