Review article: non‐malignant haematological complications of anti‐tumour necrosis factor alpha therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it