Understanding How the Diagnostic Delay of Spondyloarthritis Differs Between Women and Men: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: To identify empirical evidence of diagnostic delay in spondyloarthritis (SpA), determine whether sex-related differences persist, and conduct an analysis from that perspective of the possible causes, including the influence of quality research, in this group of inflammatory rheumatic diseases. METHODS: A systematic review was done of delay in diagnosis of SpA in MEDLINE and EMBASE and other sources. Study quality was determined in line with the Strengthening The Reporting of OBservational studies in Epidemiology (STROBE) statement. A metaanalysis of 13 papers reporting sex-disaggregated data was performed to evaluate sex-related differences in diagnostic delay. The global effect of diagnostic delay by sex was calculated using means difference (D) through a fixed effects model. RESULTS: The review included 23,883 patients (32.3% women) from 42 papers. No significant differences between the sexes were detected for symptoms at disease onset or during evolution. However, the mean for delay in diagnosis of SpA showed sex-related differences, being 8.8 years (7.4-10.1) for women and 6.5 (5.6-7.4) for men (p = 0.01). Only 40% of papers had high quality. A metaanalysis included 12,073 participants (31.2% women). The mean global effect was D = 0.6 years (0.31-0.89), indicating that men were diagnosed 0.6 year (7 months) before women. CONCLUSION: Delay in diagnosis of SpA persists, and is longer in women than in men. There are no significant sex-related differences in symptoms that could explain sex-related differences in diagnostic delay. Methodological and possible publication bias could result in sex-biased medical practice.
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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 | 0.030 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.048 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".