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Record W3041312806 · doi:10.1007/s11657-020-00767-8

Burden of musculoskeletal disorders in Iran during 1990–2017: estimates from the Global Burden of Disease Study 2017

2020· article· en· W3041312806 on OpenAlexaff
Mostafa Shahrezaee, Sara Keshtkari, Maziar Moradi‐Lakeh, Mitra Abbasifard, Vahid Alipour, Jalal Arabloo, Afsaneh Arzani, Mohammad Hossein Bakhshaei, Akbar Barzegar, Ali Bijani, Mostafa Dianatinasab, Sharareh Eskandarieh, Reza Ghanei Gheshlagh, Ahmad Ghashghaee, Reza Heidari‐Soureshjani, Seyed Sina Naghibi Irvani, Amitis Lahimchi, Shahnaz Maleki, Navid Manafi, Ali Manafi, Mohammad Alì Mansournia, Abdollah Mohammadian-Hafshejani, Mohammad Ali Mohseni Bandpei, Rahmatollah Moradzadeh, Mehdi Naderi, Keyvan Pakshir, Alireza Rafiei, Vahid Rashedi, Nima Rezaei, Aziz Rezapour, Mohammad Ali Sahraian, Saeed Shahabi, MohammadBagher Shamsi, Amin Soheili, Ali Soroush, Mohammad Zamani, Bahram Mohajer, Farshad Farzadfar

Bibliographic record

VenueArchives of Osteoporosis · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineBurden of diseaseDiseaseOrthopedic surgeryDisease burdenEnvironmental healthIntensive care medicinePhysical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.281
Teacher spread0.268 · 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 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

Citations33
Published2020
Admission routes1
Has abstractno

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Same venueArchives of OsteoporosisSame topicMusculoskeletal Disorders and RehabilitationFrench-language works237,207