Clinical Outcomes and Contributors to Weight Loss in a Cancer Cachexia Clinic
Bibliographic record
Abstract
BACKGROUND: Cancer cachexia is considered intractable, with few therapeutic options. Secondary nutrition impact symptoms (S-NIS) such as nausea may further contribute to weight loss by decreasing nutrient intake. In addition, treatable metabolic abnormalities such as hypogonadism, vitamin B12 deficiency, hypothyroidism, and hypoadrenalism could exacerbate anorexia and muscle wasting in patients with cancer cachexia. We determined the frequency and type of contributors to appetite and weight loss, and the effect of the cachexia clinic on clinical outcomes. METHODS: Review of 151 consecutive patients referred to a cachexia clinic. All received dietary counseling and exercise recommendations. Assessments included weight, body mass index (BMI), S-NIS, resting energy expenditure by indirect calorimetry, serum thyroid stimulating hormone (TSH), cortisol, total testosterone, and vitamin B12. RESULTS: Median weight loss in the 100 days before referral was 9% (4%-13%); median BMI at presentation was 20.8. Median number of S-NIS was 3 (2-4), most commonly treated by metoclopramide, laxatives, and antidepressants. Forty-one percent (24/59) of patients were hypermetabolic and 73% (52/71) of males hypogonadic, whereas hypoadrenalism (0/101, 0%), hypothyroidism (4/113, 4%), and low vitamin B12 (3/107, 3%) were uncommon. Poor appetite and weight loss before referral (r = 0.18, p = 0.036) were associated with increased S-NIS (r = 0.22, p = 0.008). Appetite improved (p < 0.001) and 31/92 (34%) of patients returning for a second visit gained weight. CONCLUSIONS: Patients had a high frequency of multiple S-NIS, hypogonadism, and hypermetabolism. A combination of simple pharmacological and nonpharmacological interventions improved appetite significantly, and increased weight in one third of patients who were able to return for follow-up. Cachexia clinics are feasible and effective for many patients with advanced cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".