Bibliographic record
Abstract
IntroductIonLymphoma represents the sixth most common form of cancer in the UK, with non-Hodgkin lymphoma (NHL) representing 4% and Hodgkin lymphoma (HL) <1% of cases; over 13 000 lymphoma cases are diagnosed annually 1,2 and its incidence, in particular NHL, has continued to rise over recent decades.3 There are now more than 40 recognised lymphoma subtypes and this contributes to its heterogeneity of clinical presentation, ranging from subtle signs and symptoms to acutely unwell cases with endorgan compromise.The recently reported Eurocare-5 study 4 addressed composite data from 30 cancer registries across Europe, including the UK, and demonstrated improvements in 5-year adjusted survival for HL and subtypes of NHL, including the two most common: diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL).However, the overall 5-year survival rate for NHL in the UK (57.4%) was inferior to the European mean (59.4%), 5 so there is a pressing need to address this disparity and the factors that may be responsible.Without the signs of a classical lymphoma presentation, patients will often visit their GP many times 6 before a possible diagnosis comes to light, often resulting in delayed referral and diagnosis.Therefore, knowledge of the common signs and symptoms is crucial and may help guide GPs to recognise the disease earlier, refer on appropriately, and avoid unnecessary diagnostic delays.The final diagnosis of lymphoma always requires histological confirmation and appropriate staging investigations.The sooner this is performed, the more likely
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".