Assessing and Managing Unintentional Weight Loss: A Global Survey of Geriatrician Practice and Their Use of Ice Cream to Address It
Bibliographic record
Abstract
OBJECTIVES: Unintentional weight loss (UIWL) is common among older adults but lacks standardized methods for its diagnosis and management. With a limited understanding on how geriatricians actually address UIWL, we conducted a survey to examine how they diagnose and manage it, and their opinions regarding the use of ice cream to address it. DESIGN, SETTING, AND PARTICIPANTS: An international descriptive, cross-sectional, online survey conducted over a 16-week period in 2019 involving 1131 geriatricians in clinical practice across 51 countries. MEASUREMENTS: We collected information around respondent demographics, use of screening tools and diagnostic investigations, and pharmacological and non-pharmacological approaches to address UIWL. RESULTS: 89.1% of respondents reported frequently seeing UIWL. The most common methods reportedly used to evaluate UIWL were performing a comprehensive history and physical examination (97.4%) and assessing for cognitive impairment (86.5%). 74.2% noted that they routinely prescribed oral nutritional supplements and 71.6% involved non-medical professional(s) to help manage UIWL. While 50.4% reported recommending ice cream to their patients with UIWL, only 30.6% reported being aware of other colleagues recommending it. Geriatricians in practice for 30+ years were significantly more likely to recommend ice cream (P < 0.05). A thematic analysis of qualitative responses identified that prescribing ice cream tended to align both with patient preferences and socio-economic realities. CONCLUSION: While a majority of geriatricians surveyed routinely prescribe ONS and involve others to manage UIWL, at least half are also recommending ice cream. A key practice amongst experienced geriatricians, the use of ice cream could be better acknowledged as a practical and cost-effective way to address UIWL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".