Bibliographic record
Abstract
Hodgkin's lymphoma usually presents with typical lymphadenopathy that has been detected either incidentally by the patient or by imaging procedures performed for assessment of other conditions. Occasionally, it may be detected when investigation of nonspecific symptoms, such as fever, fatigue, or unexplained pain prompt assessment that, in turn reveals a mass lesion. The diagnosis must be confirmed with an appropriate biopsy. Nowadays, clinicians usually have little difficulty making the diagnosis of Hodgkin's lymphoma. Knowledge of the usual pattern of spread of this lymphoma, with its orderly progression through lymph node groups and its typical forms of extranodal involvement, facilitates timely diagnosis, staging, and treatment planning. Rare manifestations due to involvement of unusual sites or presentation with paraneoplastic organ dysfunction can prove challenging but a search for mass lesions and an appreciation of these uncommonly encountered findings as potential clues to the presence of Hodgkin's lymphoma usually prompts appropriate investigation and correct diagnosis. Finally, an understanding of the usual pattern and timing of relapse and knowledge of the typical types of late toxicity expected after successful eradication of the lymphoma allow the patient's physicians to detect recurrence in a timely fashion and to identify or prevent secondary complications enabling appropriate management plans to be developed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".