Mortality from Musculoskeletal Disorders Including Rheumatoid Arthritis in Southern Sweden: A Multiple-cause-of-death Analysis, 1998–2014
Bibliographic record
Abstract
OBJECTIVE: To assess mortality related to musculoskeletal (MSK) disorders and rheumatoid arthritis (RA), specifically, among adults (aged ≥ 20 yrs) in southern Sweden using the multiple-cause-of-death approach. METHODS: All death certificates (DC; n = 201,488) from 1998 to 2014 for adults in the region of Skåne were analyzed when mortality from MSK disorders and RA was listed as the underlying and nonunderlying cause of death (UCD/NUCD). Trends in age-standardized mortality rates (ASMR) were evaluated using joinpoint regression, and associated causes were identified by age- and sex-adjusted observed/expected ratios. RESULTS: MSK (RA) was mentioned on 2.8% (0.8%) of all DC and selected as UCD in 0.6% (0.2%), with higher values among women. Proportion of MSK disorder deaths from all deaths increased from 2.7% in 1998 to 3.1% in 2014, and declined from 0.9% to 0.5% for RA. The mean age at death was higher in DC with mention of MSK/RA than in DC without. The mean ASMR for MSK (RA) was 15.5 (4.3) per 100,000 person-years and declined by 1.1% (3.8%) per year during 1998-2014. When MSK/RA were UCD, pneumonia and heart failure were the main NUCD. When MSK/RA were NUCD, the leading UCD were ischemic heart disease and neoplasms. The greatest observed/expected ratios were seen for infectious diseases (including sepsis) and blood diseases. CONCLUSION: We observed significant reduction in MSK and RA mortality rates and increase in the mean age at death. Further analyses are required to investigate determinants of these improvements in MSK/RA survival and their potential effect on the Swedish healthcare systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".