Addressing the unmet need for salivary gland cancers in the UK and beyond
Bibliographic record
Abstract
Background Salivary Gland Cancers (SGC) are rare. Over 23 distinct types account for only c.0.3% of cancer cases globally each year. They represent less than 5% of all head and neck cancers and encompass ‘salivary type’ histologies presenting in other areas of the body in the secretory glands, including the lacrimal gland, trachea and the vulva. There is an unmet need for a salivary cancer specific advocacy group to support patients and provide support for the development of new treatment options to improve outcomes for patients. Methods We established Salivary Gland Cancer UK (SGC-UK) which is a unique charity world-wide focused specifically on these cancers. A collaboration between a patient advocate and a salivary cancer medical oncologist it was launched in April 2019 to address the unmet need for patients, carers and those treating and researching these cancers. Using co-production to build an active patient and research community, SGC UK is working to advance understanding of SGC biology, advance research, develop new treatments, and support patients and carers. Results Patients with all SGC types are supported. Patients from across the UK can have clinical input through a specialist hub at The Christie NHS Foundation Trust, Manchester, UK A biobank of tumour and blood samples has been established supporting national and international research Regular national/international meetings build and support the community, provide networking opportunities and research updates Focus over the last 24 months was SGC pathology/genomics, lab studies, carbon ion treatment, and real-world outcomes Researcher – Patient co-production via discussion days informing organisational priorities and aims ensuring relevance and impact within the patient population Reliable patient information was co-produced including ‘Could gene profiling help you' information sheets and return to work guides including real-life stories and videos from our network empowering patients/advocates. Conclusions SGC-UK is a unique collaboration addressing a significant unmet need. A biobank/database is being established to drive forward national and international research. Reliable information and support is being provided to patients, carers and clinicians. Collaboration and co-production to benefit all.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 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; 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".