Methodological Issues with Head and Neck Cancer Prognostic Risk Prediction Models.
Bibliographic record
Abstract
OBJECTIVE: Prognostic risk prediction models estimate the probability of developing head and neck cancer (HNC), providing valuable information for managing the disease. While different prognostic HNC risk prediction models have been developed worldwide, a comprehensive evaluation of their methods is lacking. We conducted a scoping review with a critical assessment aiming to identify the methodological strengths and limitations of HNC risk prediction models. METHOD: We searched Medline, Embase, Scopus, Web of Science, and CAB Abstracts databases and included full-text-available peer-reviewed published papers on developing or validating a prognostic HNC risk prediction model. Study quality was appraised using the PROBAST tool. RESULTS: Nine papers were included. Although all had a high risk of bias, mainly in the analysis domain, only two studies had high concerns about clinical applicability. CONCLUSION: Currently published studies provide insufficient information on methods, making it difficult to judge the models' quality and applicability. Future investigations should follow the guidelines in reporting the prediction modelling studies.
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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 | 0.431 | 0.660 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".