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Record W2739107731 · doi:10.1093/rheumatology/kex255

Comment on: Cumulative immunosuppressant exposure is associated with diversified cancer risk among 14 832 patients with systemic lupus erythematosus

2017· letter· en· W2739107731 on OpenAlexaff
Sasha Bernatsky, Ann E. Clarke, Rosalind Ramsey‐Goldman

Bibliographic record

VenueLara D. Veeken · 2017
Typeletter
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of CalgaryMcGill University Health Centre
Fundersnot available
KeywordsMedicineCumulative doseSystemic lupusCumulative riskCancerSystemic lupus erythematosusLupus erythematosusInternal medicineDermatologyOncologyImmunologyDisease

Abstract

fetched live from OpenAlex

Sir, We read with interest the study by Hsu et al. [1]. The authors’ use of catastrophic illness certificates is an admirable way of improving SLE identification within administrative data, although it is noteworthy that almost half of the subjects had an overlap with another rheumatic disease. The purpose of this letter is to point out three other interesting aspects of the study. The first was the authors’ attention to death as a competing risk. A competing risk is defined as an outcome that is of equal or greater importance than the primary outcome. However, by removing all SLE subjects who died, the authors risk introducing survivor bias as the remaining population will be healthier than the reference population [2]. The authors should consider repeating their analyses using an alternative statistical approach to account for the presence of competing risks. The second aspect of the study that we would like to comment on are the findings that although cyclophosphamide increased the overall cancer risk, anti-malarial treatment decreased the overall cancer risk. The multivariate analyses adjusted for various factors but they apparently did not adjust for the concomitant use of CYC, other immunosuppressants and anti-malarial drugs. We wonder whether a model that adjusted concomitantly for these exposures would reproduce the same results. Finally, the authors considered all cancer types as one outcome, though it may be that certain malignancies (e.g. haematological) are more likely to be associated with a medication like CYC (as opposed to lung cancer perhaps, where other factors, such as smoking, may be more important [3]). As the authors point out, 70% of their SLE cancer cases were not exposed to CYC. Alternative hypotheses for an increased risk of diffuse large B cell lymphoma (DLBCL) in SLE include genetic factors. Attempts to identify an increased occurrence of known susceptibility loci for DLBCL in SLE patients have not yet been fruitful [4]; however, using a huge dataset from recent InterLymph genome-wide association studies, two SLE-related single nucleotide polymorphisms were clearly associated with risk of DLBCL. These included the rs3024505 single nucleotide polymorphism on chromosome 1, a variant allele of IL10 (odds ratio per risk allele = 1.14; 95% CI: 1.05, 1.23), and the HLA SLE risk allele rs1270942 on chromosome 6 (odds ratio per risk allele = 1.20; 95% CI: 1.08, 1.33) [5]. In closing, we strongly encourage the authors to consider publishing additional results using statistical approaches to competing risk that avoid bias, and to adjust for concomitant use of CYC and anti-malarial drugs. Though additional analyses looking at the effects of drugs on specific cancer types do impose power limitations, these are also necessary to provide the most meaningful results. Funding: No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this manuscript. Disclosure statement: The authors have declared no conflicts of interest.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0240.014
Insufficient payload (model declined to judge)0.0050.005

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.

Opus teacher head0.023
GPT teacher head0.270
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations1
Published2017
Admission routes1
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