Essential data variables for a minimum dataset for head and neck cancer trials and clinical research: HNCIG consensus recommendations and database
Bibliographic record
Abstract
The Head and Neck Cancer International Group (HNCIG) has undertaken an international modified Delphi process to reach consensus on the essential data variables to be included in a minimum database for HNC research. Endorsed by 19 research organisations representing 34 countries, these recommendations provide the framework to facilitate and harmonise data collection and sharing for HNC research. These variables have also been incorporated into a ready to use downloadable HNCIG minimum database, available from the HNCIG website.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.407 | 0.568 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".