Frequency of antiphosholipid antibodies in Iranian patients with solid malignancies: a pilot study.
Bibliographic record
Abstract
LETTER TO EDITOR Antiphospholipid antibodies (aPLs) are a family of immunoglobulins acting against different phospholipids or their complexes with plasma proteins. Included in this family are lupus anticoagulant (LA), antibodies to cardiolipin antibodies (aCLA) and to beta2 glycoprotein I (aβ2GP1). Some clinical manifestations associated with aPLs are venous and arterial thromboses, thrombocytopenia, hemolytic anemia, obstetric complications as recurrent spontaneous abortion, and livedo reticularis (1). APLs are reported in patients with hematological malignancies and solid tumors such as β-cell lymphoma, non–Hodgkin’s lymphoma (NHL), chronic myeloid leukemia (CML), renal cell carcinoma, melanoma and lung cancer (2). Previous studies have shown that aPLs increase in cancer patients and this may be a contributing factor to the increased incidence of thromboembolism events like venous thrombosis and pulmonary embolism in these patients which in turn is associated with poor prognosis (3). That is why some suggest that aPLs may be considered as markers for disease activity and progression in certain malignancies because of their effects on mortality and morbidity during the course of cancer (4). On the other hand, some authors suggest increased prevalence of certain cancers in patients with aPLs (5). Other studies showed no relation between aPLs and certain cancers (6). It seems that aPLs are important factors to study in cancer patients. Therefore we studied a group of Iranian patients with different types of malignancies to measure aPL titers and to evaluate possible association with malignancies. 52 patients including 20 males and 32 females with different types of solid malignancies were randomly enrolled from Nemazee Hospital, Southern Iran, during their routine follow up from June 2006 to May 2007. Sera were prepared and stored at -20°C until analysed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".