Predicting short-term treatment toxicity in head and neck cancer through a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Frailty is a recognized condition associated with poorer outcomes in patients with head and neck cancer (HNC). The objective of this study was to ascertain the prognostic significance of various frailty metrics on short-term treatment toxicity in patients with HNC undergoing curative-intent therapy. MATERIALS AND METHODS: A systematic review was performed searching multiple databases. An inverse-variation, random-effects model was used to perform the meta-analysis to evaluate the prognostic significance of various frailty metrics on short-term treatment-related toxicity in this population. RESULTS: A total of 292,560 patients with HNC originating from 36 observational studies were analyzed. The most frequently reported frailty metrics were the modified frailty index (mFI), Geriatric 8 questionnaire (G8), Adjusted Clinical Groups (ACG), Groningen Frailty Indicator (GFI), and comprehensive geriatric assessment (CGA). The overall prevalence of frailty using any metric in all included studies was 7.5 %. The combined odds ratio (OR) for short-term treatment toxicity using the mFI was 2.60 (95 % CI of 1.81-3.72), G8 2.69 (95 % CI 1.37-5.28), ACG 3.43 (95 %CI 2.52-4.67), GFI 2.71 (95 % CI 1.11-6.62), and CGA 3.36 (95 % CI 1.18-9.53). The association of frailty with short-term treatment toxicity using various frailty metrics was more pronounced in patients with upfront surgery (OR 3.00, 95 %CI of 2.35-3.81) compared to definitive (chemo)radiotherapy 2.64 (95 % CI 1.04-6.68). DISCUSSION: Various frailty metrics exists in the HNC literature, with the most common being the mFI, G8, ACG, GFI, and CGA. Patients with HNC and frailty are more than twice as likely to suffer a short-term treatment-related toxicity when undergoing curative-intent HNC treatment than patients without frailty. This effect is more pronounced in patients undergoing upfront surgery.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".