Translating Evidence-Based Guidelines into Practice—Are We Getting It Right? A Multi-Centre Prospective International Audit of Nutrition Care in Patients with Foregut Tumors (INFORM)
Bibliographic record
Abstract
Malnutrition is highly prevalent in patients with foregut tumors comprising head and neck (HNC) and esophageal (EC) cancers, negatively impacting outcomes. International evidence-based guidelines (EBGs) for nutrition care exist; however, translation of research evidence into practice commonly presents considerable challenges and consequently lags. This study aimed to describe and evaluate current international nutrition care practices compared with the best-available evidence for patients with foregut tumors who are at high risk of malnutrition. A multi-centre prospective cohort study enrolled 170 patients commencing treatment of curative intent for HNC (n = 119) or EC (n = 51) in 11 cancer care settings in North America, Europe and Australia between 2016 and 2018. Adherence criteria were derived from relevant EBG recommendations with pooled results for participating centres reported according to the Nutrition Care Model at either system or patient levels. Adherence to EBG recommendations was: good (≥80%) for performing baseline nutrition screening and assessment, perioperative nutrition assessment and nutrition prescription for energy and protein targets; moderate (≥60 to 80%) for utilizing validated screening and assessment tools and pre-radiotherapy dietitian consultation; and poor (60%) for initiating post-operative nutrition support within 24 h and also dietetic consultation weekly during radiotherapy and fortnightly for 6 weeks post-radiotherapy. In conclusion, gaps in evidence-based cancer nutrition care remain; however, this may be improved by filling known evidence gaps through high-quality research with a concurrent evolution of EBGs to also encompass practical implementation guidance. These should aim to support multidisciplinary cancer clinicians to close evidence–practice gaps throughout the patient care trajectory with clearly defined roles and responsibilities that also address patient-reported concerns.
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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 | 0.150 | 0.339 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".