Oncology-Led Early Identification of Nutritional Risk: A Pragmatic, Evidence-Based Protocol (PRONTO)
Bibliographic record
Abstract
Nutritional issues, including malnutrition, low muscle mass, sarcopenia (i.e., low muscle mass and strength), and cachexia (i.e., weight loss characterized by a continuous decline in skeletal muscle mass, with or without fat loss), are commonly experienced by patients with cancer at all stages of disease. Cancer cachexia may be associated with poor nutritional status and can compromise a patient's ability to tolerate antineoplastic therapy, increase the likelihood of post-surgical complications, and impact long-term outcomes including survival, quality of life, and function. One of the primary nutritional problems these patients experience is malnutrition, of which muscle depletion represents a clinically relevant feature. There have been recent calls for nutritional screening, assessment, treatment, and monitoring as a consistent component of care for all patients diagnosed with cancer. To achieve this, there is a need for a standardized approach to enable oncologists to identify patients commencing and undergoing antineoplastic therapy who are or who may be at risk of malnutrition and/or muscle depletion. This approach should not replace existing tools used in the dietitian's role, but rather give the oncologist a simple nutritional protocol for optimization of the patient care pathway where this is needed. Given the considerable time constraints in day-to-day oncology practice, any such approach must be simple and quick to implement so that oncologists can flag individual patients for further evaluation and follow-up with appropriate members of the multidisciplinary care team. To enable the rapid and routine identification of patients with or at risk of malnutrition and/or muscle depletion, an expert panel of nutrition specialists and practicing oncologists developed the PROtocol for NuTritional risk in Oncology (PRONTO). The protocol enables the rapid identification of patients with or at risk of malnutrition and/or muscle depletion and provides guidance on next steps. The protocol is adaptable to multiple settings and countries, which makes implementation feasible by oncologists and may optimize patient outcomes. We advise the use of this protocol in countries/clinical scenarios where a specialized approach to nutrition assessment and care is not available.
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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 | 0.060 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".