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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".