Review article: non‐malignant haematological complications of anti‐tumour necrosis factor alpha therapy
Bibliographic record
Abstract
BACKGROUND: Tumour necrosis factor-alpha (TNF-α) is an important mediator of the molecular cascade leading to chronic inflammation. TNF-α inhibitors have proven their safety and efficacy in the treatment of inflammatory diseases. AIM: To review the non-malignant haematological adverse events, such as thrombocytopaenia, neutropaenia, hypercoagulability, pancytopaenia and aplastic anaemia in patients receiving TNF-α inhibitors. METHODS: We reviewed the literature by searching MEDLINE and EMBASE databases as well as references of all retrieved articles for the following terms: anti-tumour necrosis factor, anti-TNF, infliximab, adalimumab, certolizumab, etanercept, haematological complications, thrombocytopaenia, neutropaenia, anaemia, bone marrow and thrombosis. RESULTS: Thombocytopaenia is a very rare phenomenon and was associated with no serious adverse events. However, transient neutropaenia developed in up to 16% of cases. Patients with a previous history of neutropaenia on other therapies or baseline neutrophil count <4 × 10(9) /L are at a particularly higher risk. The association between anti-TNF-α therapy and thrombosis is very nebulous due to the multitude of potential confounders. Only one case of primary eosinophilia has been reported with anti-TNF-α therapy. CONCLUSION: Regular monitoring of the white blood cell count at baseline and with each infusion is recommended for patients on anti-TNF-α. Further studies to elucidate their interaction with the immune system are warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".