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Record W4401537520 · doi:10.1371/journal.pone.0308625

Is educational attainment associated with the onset and outcomes of low back pain? a systematic review and meta-analysis

2024· review· en· W4401537520 on OpenAlexaff
Aliyu Lawan, Alex Aubertin, Jane Mical, Joanne Hum, Michelle L. Graf, Peter Marley, Zachary Bolton, David M. Walton

Bibliographic record

VenuePLoS ONE · 2024
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of British ColumbiaCambrian CollegeFraser HealthWestern University
Fundersnot available
KeywordsMedicineCINAHLMeta-analysisBiopsychosocial modelMEDLINEPopulationSocioeconomic statusPhysical therapyLow back painPublication biasInternal medicinePsychological interventionAlternative medicineEnvironmental healthPsychiatryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Low back pain (LBP) is the leading global cause of years lived with disability. Of the biopsychosocial domains of health, social determinants of LBP remain under-researched. Socioeconomic status (SES) may be associated with the onset of new LBP or outcomes of acute LBP, with educational attainment (EA) being a key component of SES. The association between EA and LBP has yet to be the subject of a dedicated review and meta-analysis. PURPOSE: To review evidence of the association between EA and a) onset or b) outcomes of acute and subacute LBP in the adult general population and to conduct statistical pooling of data where possible. METHODS: An electronic search was conducted in MEDLINE, Embase, CINAHL, and ProQuest from inception to 2nd November 2023 including reference lists to identify relevant prospective studies. Risk of bias (RoB) was assessed using the Quality in Prognostic Studies (QUIPS) tool. Where adequate data were available, estimates were pooled using a random-effects meta-analysis. Overall evidence for each outcome was graded using an adapted GRADE. RESULTS: After screening 8498 studies, 29 were included in the review. Study confounding and attrition were common biases. Data from 19 studies were statistically pooled to explore EA as a predictor of new LBP onset or as prognostic for outcomes of acute or subacute LBP. Pooled results showed no association between EA and the onset of new LBP (OR: 0.927, 95%CI: 0.747 to 1.150; I2 = 0%). For predicting outcomes of acute LBP, compared to those with no more than secondary-level education, post-secondary education or higher was associated with better outcomes of pain (OR: 0.538, 95%CI: 0.432 to 0.671; I2 = 35%) or disability (OR: 0.565, 95%CI: 0.420 to 0.759; I2 = 44%). High heterogeneity (I2>80%) prevented meaningful pooling of estimates for subacute LBP outcomes. CONCLUSION: We found no consistent evidence that lower EA increases the risk of LBP onset. Lower EA shows a consistent association with worse LBP outcomes measured at least 3 months later after acute onset with inconclusive findings in subacute LBP. Causation cannot be supported owing to study designs. High-quality research is needed on potential mechanisms to explain these effects.

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 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)0.018
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.044
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0230.045
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.103
GPT teacher head0.347
Teacher spread0.244 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes1
Has abstractyes

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