Comment on: The impact of smoking on prevalence of psoriasis and psoriatic arthritis: reply
Bibliographic record
Abstract
Dear Editor, We read the letter by Zhao et al. in response to our manuscript, ‘The impact of smoking on prevalence of psoriasis and psoriatic arthritis’ [1, 2]. Our systematic literature review and meta-analysis demonstrated that the prevalence of ever smoking is increased in psoriasis compared with the general population whereas the prevalence of ever smoking is reduced within psoriasis patients and not different from the general population. As already stated in our manuscript, the conflicting results between the prevalence of smoking in psoriasis vs PsA can certainly be due to a methodological bias and may be a result of the collider effect. As can be read in the manuscript, all findings have been reported with ‘possibilities’ and not statements, highlighting the potential effect of the methodological bias in every step. The authors question our raising a hypothesis on potential biological effects of smoking and mention ‘biological implausibility’. We would like to point out the ongoing debate and numerous observations on the effect of smoking and ulcerative colitis and Behçet’s disease flares [3–5]. In our view, in the absence of any data that specifically seeks and investigates causality, it is not possible to rule out any effect of any variable on the outcomes, in any direction, and it is important for a researcher to be open minded. We believe, as in this case in which there are conflicting results in the literature, instead of unquestionably accepting this all being due to a methodological error, it is prudent to make observations, raise hypotheses and perform specific studies to look for the causality. Our abstract clearly refers to the observation in PsA that may be due to the collider bias. We also did not include the results of the analysis in the title on purpose (e.g. reduced risk of PsA among smokers), so as not to mislead the readers without knowing the limitations of performing a meta-analysis in PsA, which is discussed in the manuscript. Similar to Zhao et al. we also have an appetite for research that focuses on causality, which again already has been stated as a need in the discussion of our manuscript. It is challenging to seek for causality in this scenario as patients cannot be exposed to the risk of smoking, the duration between psoriasis and arthritis can be highly variable and may be >20 years, and longitudinal studies for a few years would not be able to demonstrate the actual effect. Due to these challenges, we believe that the available data should not be ignored but interpreted with caution, and that has been our aim throughout the manuscript. Finally, all data included in the manuscript have compared ‘the prevalence of smoking in psoriasis patients compared with the general population’ and not ‘the prevalence of psoriasis among smokers compared with non-smokers in the general population’. We noticed some errors in the wording of the results section where it reads as ‘the prevalence of PsA among smokers’ which was not investigated in our review. We sincerely thank the authors for the opportunity to clarify the results in PsA. In conclusion, there are conflicting results about the prevalence of smoking in PsA. These observations may be due to a methodological bias. However, we believe rather than ignoring these observations, research that specifically seeks for the effect of smoking in the disease course needs to be initiated. Funding: No specific funding was received from any funding 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.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.056 | 0.041 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".