Prognostic factors for mortality in bullous pemphigoid: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically evaluate the prognostic factors for mortality in bullous pemphigoid. METHODS: PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disc and Wanfang Database were searched to collect literature on the prognostic factors for mortality in bullous pemphigoid. The quality of studies was assessed by Newcastle-Ottawa Quality Assessment Scale. Two researchers extracted relevant data and scored study quality independently. The hazard ratio (HR) was calculated using the random effects model. Study heterogeneity was assessed using both Cochran's Q test and I2 statistics. The causes of heterogeneity were assessed by subgroup analysis and/ or sensitivity analysis when heterogeneity was significant. When ten or more studies were included as outcome indicators, publication bias was evaluated by funnel plot and Egger's test. RESULTS: Out of a total of 1,546 articles retrieved, 15 studies involving 2,435 patients were included. The meta-analysis showed that the mortality of patients with bullous pemphigoid increased with positive bullous pemphigoid 180 antibody (HR = 1.85, 95%CI: 1.25~2.75, P = 0.002); concomitant dementia (HR = 2.26, 95%CI: 1.43~3.59, P<0.001); stroke (HR = 2.09, 95% CI: 1.23-3.55, P = 0.007); heart disease (HR = 1.96, 95% CI: 1.41-2.73, P<0.001) and diabetes mellitus (HR = 2.39, 95% CI: 1.55-3.69, P<0.001). Sex, positive indirect immunofluorescence and hypertension were not associated with prognosis. CONCLUSION: Positive bullous pemphigoid 180 antibody, dementia, stroke, heart disease and diabetes mellitus were the prognostic factors for mortality in bullous pemphigoid.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".