Acute appendicitis in Japanese soldiers in Burma: support for the “fibre” theory
Bibliographic record
Abstract
Immunosuppression, IBD, and risk of lymphomaWe read with interest the two recent reports of lymphoma in patients with inflammatory bowel disease (IBD) (Farrell et al, Gut 2000;47:514-19 and Palli et al, Gastroenterology 2000;119:647-53).We believe the report from Farrell of four cases of lymphoma in a cohort of 782 patients (of whom 238 had received immunosuppression) considerably overestimates the relative risk of lymphoma in IBD patients.They calculate a relative risk of lymphoma as 31 for the whole cohort and 59 for the group treated with immunosuppressives (compared with the general population).Immunosuppressive therapy is well recognised as increasing the risk of developing non-Hodgkin's lymphoma (NHL) in organ transplant patients.1 The risk of NHL is increased in other inflammatory conditions, such as rheumatoid arthritis 2 and psoriasis, although how much is attributable to the underlying disease and how much is due to the drug is unclear.For IBD, if the incidence of lymphoma is indeed increased, is this due to drug or disease?Two recent reviews 3 4 have examined this question in detail.Several large well designed population based studies have been performed specifically to examine the baseline risk of lymphoma in IBD.In none of these studies does the relative risk for NHL significantly exceed one, while only one study (Palli et al) has shown an excess risk of Hodgkin's disease (relative risk 9.3; 95% confidence interval 2.5-23.8).A number of smaller case series have been published which show an increased incidence of NHL.This type of study, although interesting, should not be regarded as evidence of increased risk as case ascertainment bias is likely to exist.Several studies have specifically addressed the question of immunosuppression in IBD.In total, only 11 cases of lymphoma were described in more than 4000 patients who had received immunotherapy, with over 17 000 patient years of follow up.Extrapolating these data to lymphoma rates in the general population may be unreliable, particularly as lymphoma rates vary widely geographically, by sex and age. 5 We believe that compared with the other known risks of immunosuppression, such as myelosuppression and infection, the risk of developing lymphoma (if it does exist) is likely to be of minor clinical significance and to be outweighed by the potential benefit of these treatments in patients with IBD.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.006 |
| 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; 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".