Poor nutrition status associated with low patient satisfaction six months into treatment for head and neck/esophageal cancer treatment: A prospective multicenter cohort study
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures have been associated with survival in oncology patients. Altered intake and malnutrition are common symptoms for patients treated for head and neck cancer and esophageal cancer (HNC/EC). The purpose of this study was to examine the relationship between patient-reported satisfaction with medical care and nutrition status. METHODS: This prospective cohort study collected data from 11 international cancer care sites. RESULTS: One hundred and sixtythree adult patients (n = 115 HNC; n = 48 EC) completed a patient satisfaction questionnaire (the Canadian Health Care Evaluation Project Lite) and were included. HNC/EC patient global satisfaction with medical care was 88.3/100 ± 15.3 at baseline and remained high at 86.6/100 ± 16.8 by 6 months (100 max satisfaction score). Poor nutrition status, as defined by the Patient-Generated Subjective Global Assessment Short Form, was associated with lower patient satisfaction with overall medical care, relationship with doctors, illness management, communication, and decision-making 6 months into treatment (P < 0.01). There was no difference in global satisfaction between patients who did and did not report swallowing difficulty (P = 0.99) and patients with and without feeding tube placement (P = 0.36). Patients who were seen by a dietitian for at least one nutrition assessment had global satisfaction with care that was 16.7 percentage points higher than those with no nutrition assessment (89.3 ± 13.8 vs 72.6 ± 23.6; P = 0.005) CONCLUSION: In HNC/EC patient-centered oncology care, decreasing malnutrition risk and providing access to dietitian-led nutrition assessments should be prioritized and supported to improve patient satisfaction and standard of care. Feeding tube placement did not decrease patient satisfaction with medical 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".