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Comment to the article: Numerous factors hamper objective assessment of disease activity in axial spondyloarthritis

2024· article· en· W4408090263 on OpenAlexaboutno aff
Özenç İnan, Ebru Aytekin, Yasemin Pekin Doğan, İlhan Nahit Mutlu, Kübra Aydemir, Nuran Öz, Nil Sayıner Çağlar

Bibliographic record

VenueArchives of Rheumatology · 2024
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAxial spondyloarthritisMedicinePhysical therapyDiseaseIntensive care medicineTraditional medicineInternal medicineSacroiliitis

Abstract

fetched live from OpenAlex

We read your letter with interest and would like to respond to some points that need clarification. First of all, we would like to recall that the study was based on the current imaging and disease activity scores of patients with axial spondyloarthritis (axSpA) diagnosed within Assessment of Spondyloarthritis International Society (ASAS) classification criteria. The study design was not based on newly diagnosed patients or retrospective data. Therefore, we did not include additional information regarding the identification of patient groups from the Human Leukocyte Antigen B27 (HLA-B27) arm and/or imaging arm at the time of diagnosis. Indeed, the literature reports that within 2-10 years, 10-40% of patients with nonradiographic axSpA may progress to radiographic axSpA.[1] Similarly, another study reported that bone marrow edema in the sacroiliac joint (SIJ) may change over the years in magnetic resonance imaging (MRI) follow-up of patients with axSpA.[2] In that study, the proportion of patients with the positive MRI definition of ASAS decreased from 29.3% to 22% at five-year MRI follow-up. Therefore, this study aimed to examine the correlation of current disease activity scores with the Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system based on current MRI imaging rather than at the time of diagnosis. We agree that the correlation coefficient is likely to be higher in HLA-B27-negative patients than in HLA-B27-positive patients. However, this criticism may be meaningful in cases where a significant correlation between disease activity scores and SPARCC scores in HLA-B27-positive patients has been reported and it has been argued that SPARCC scores may be important indicators of disease activity in HLA-B27-positive patients. In our study, only a correlation between the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), a disease activity score, and SPARCC scores was reported in HLA-B27-negative patients. The percentage of patients using anti-tumor necrosis factor (TNF) agents in Table 2 was entered incorrectly during data transfer. In our study, 12.5% (n=4) of the patients were using anti-TNF agents. We have discussed the tendency of anti-TNF agents to improve MRI scores in SIJ in our discussion section.[3] On the other hand, although controversial, non-steroidal anti-inflammatory drugs may also decrease bone marrow edeme and SPARCC scores in SIJ.[4] Additionally, anti-TNF agents are known to be highly effective in improving disease activity scores, in addition to improving MRI scores.[5] From this perspective, we would like to point out that the type of drug may affect both poles of the correlation to different degrees. In this study, correlation analysis of disease activity and SPARCC scores was included instead of a correlation analysis according to the type of drug used. Nevertheless, your contribution is valuable in terms of opening the door to new studies investigating differences according to the type of drug used. In conclusion, there are many factors that may affect correlation analyses in axSpA patients. By eliminating these factors as much as possible, healthier results can be reported.

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)0.006
metaresearch head score (Gemma)0.049
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 score0.035
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.005
Open science0.0040.001
Research integrity0.0350.036
Insufficient payload (model declined to judge)0.0060.010

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 head0.011
GPT teacher head0.294
Teacher spread0.283 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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