Weight loss as primary indication for FDG-PET/CT
Bibliographic record
Abstract
PURPOSE: Some consider fluorodeoxyglucose positron emission tomography with computed tomography (FDG-PET/CT) clinically useful in patients presenting with nonspecific symptoms of malignancy, weight loss most commonly encountered. However, the appropriateness of such FDG-PET/CT studies remains to be clarified. This study evaluated the clinical value of FDG-PET/CT in patients referred primarily for weight loss. METHODS: From 2010 to 2017 in one academic center, 252 subjects underwent 254 FDG-PET/CT studies for weight loss as primary indication and retrospectively studied. Eighteen subjects were excluded due to ongoing active malignancy, weight loss not ultimately being the main indication for the FDG-PET/CT, technically inadequate FDG-PET/CT and insufficient follow-up. The FDG-PET/CT scans were considered clinically beneficial when true positive for the cause of weight loss that other investigations missed or would have missed, clinically neutral when true negative and clinically detrimental when false positive leading to additional investigations or false negative. RESULTS: Ultimately 234 unique subjects (236 FDG-PET/CT studies) were included. The average subject weight loss prior to the PET was 12 kg and average follow-up time post FDG-PET/CT scan was 3.4 years. The FDG-PET/CT scans were true positive in 24 studies (10%) with 8 studies (3%) clinically beneficial; false positive in 38 studies (16%) of which 26 led to 35 additional procedures and false negative in 13 studies (6%). In total, 39 (17%) FDG-PET/CT studies were clinically detrimental. The other 149 (63%) studies were true negative, clinically neutral. CONCLUSION: FDG-PET/CT appears to have limited value in assessing subjects with weight loss as the leading clinical indication, proving to be five times more often detrimental than beneficial.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".