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Record W2341054551 · doi:10.2217/pmt.16.8

Reporting on Work-Related Low Back Pain: Data Sources, Discrepancies and The Art of Discovering Truths

2016· article· en· W2341054551 on OpenAlex
Xiangning Fan, Sebastian Straube

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePain Management · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
FundersDaiichi Sankyo EuropeUniversity of Alberta
KeywordsMedicineContext (archaeology)Coding (social sciences)Work (physics)Data collectionAggregate dataProductivityBack painLow back painAlternative medicineSocial scienceEngineeringEconomicsSociologyEconomic growth

Abstract

fetched live from OpenAlex

Work-related pain is unique in the pain context as it is, in theory, tied to one or more workplace activities and is therefore preventable. Back pain is a leading cause of lost workplace productivity, absence from work and reduced quality of life. Aggregate estimates of the work-related contribution to the overall burden of back pain vary, which may reflect incomplete reporting, inconsistency in data collection and coding between studies and jurisdictions, or, alternatively, genuine differences between occupational groups and countries. It is therefore important for researchers, policy analysts and program development personnel in the fields of pain medicine and occupational medicine to have a thorough understanding of the appropriate use and inherent limitations of the data sources which report on this topic.

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.

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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.018
GPT teacher head0.261
Teacher spread0.242 · 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