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Record W4405396180 · doi:10.61186/johe.13.1.10

Mortality and Years of Life Lost (YLL) Trends in Musculoskeletal Disorders in the South of Iran, 2004-2019

2024· article· en· W4405396180 on OpenAlexaff
Habibollah Azarbakhsh, Jafar Hassanzadeh, Elahe Piraee

Bibliographic record

VenueJournal of Occupational Health and Epidemiology · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsWestern University
FundersShiraz UniversityShiraz University of Medical Sciences
KeywordsYears of potential life lostMedicineDemographyGerontologyEnvironmental healthLife expectancySociologyPopulation

Abstract

fetched live from OpenAlex

Background: Musculoskeletal Disorders (MSDs) are damage to the tissue of the musculoskeletal system that disrupt the flow and functionality of the human body.The current study evaluated changes in years of life lost (YLL) due to MSDs and mortality rates associated with MSDs in the 16 year-period from 2004 to 2019.Materials and Methods: In this descriptive study, YLL for MSDs for the years 2004-2019 in Fars province was calculated using the YLL template from World Health Organization (WHO), and the number of total deaths due to MSDs in Fars province was obtained from the electronic populationbased death registration system (EDRS).Results: Between the years 2004 and 2019, 746 deaths due to MSDs occurred in Fars province.The crude mortality rate increased in men from 1.29 (per 100,000 population) in 2004 to 1.47 in 2019 (p-value for trend=0.057)and in women from 1.18 in 2004 to 2.58 in 2019 (p-value for trend <0.001).Total YLL due to MSDs over the same period was 4,690 and 6,852 (0.14 and 0.22 per 1000 population) in men and women, respectively (female/male gender ratio =1.46). Conclusion:The findings revealed that YLL due to MSDs was higher in females than males.The highest and lowest YLL due to MSDs was seen in the age group of 40-49 and 0-9 years, respectively.To minimize the effects of MSDs, further adjustment in the policies and regulations tailored for appropriate age groups and populations is recommended.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.432
Teacher spread0.356 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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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