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Record W2528465197 · doi:10.1002/ejp.931

Clinical practice guidelines for the noninvasive management of low back pain: A systematic review by the Ontario Protocol for Traffic Injury Management (OPTIMa) Collaboration

2016· review· en· W2528465197 on OpenAlexafffundabout
Jessica J. Wong, Pierre Côté, Deborah Sutton, Kristi Randhawa, Hainan Yu, Sharanya Varatharajan, Rachel Goldgrub, Margareta Nordin, Douglas P. Gross, Heather M. Shearer, Linda Carroll, Paula Stern, Arthur Ameis, Danielle Southerst, Silvano Mior, Maja Stupar, Thepikaa Varatharajan, Anne Taylor‐Vaisey

Bibliographic record

VenueEuropean Journal of Pain · 2016
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMount Sinai HospitalCentre for Disability Prevention and Rehabilitation123 Certification (Canada)University of SaskatchewanUniversité de MontréalUniversity of AlbertaOntario Tech UniversityCanadian Memorial Chiropractic College
FundersCanadian Institutes of Health ResearchCanada Research ChairsFinancial Services CommissionUniversity of Ontario Institute of Technology
KeywordsMedicineCINAHLManual therapyPhysical therapyChiropracticSpinal manipulationMEDLINECritical appraisalLow back painAcupunctureProtocol (science)RehabilitationSystematic reviewAlternative medicinePsychological interventionNursing

Abstract

fetched live from OpenAlex

We conducted a systematic review of guidelines on the management of low back pain (LBP) to assess their methodological quality and guide care. We synthesized guidelines on the management of LBP published from 2005 to 2014 following best evidence synthesis principles. We searched MEDLINE, EMBASE, CINAHL, PsycINFO, Cochrane, DARE, National Health Services Economic Evaluation Database, Health Technology Assessment Database, Index to Chiropractic Literature and grey literature. Independent reviewers critically appraised eligible guidelines using AGREE II criteria. We screened 2504 citations; 13 guidelines were eligible for critical appraisal, and 10 had a low risk of bias. According to high-quality guidelines: (1) all patients with acute or chronic LBP should receive education, reassurance and instruction on self-management options; (2) patients with acute LBP should be encouraged to return to activity and may benefit from paracetamol, nonsteroidal anti-inflammatory drugs (NSAIDs), or spinal manipulation; (3) the management of chronic LBP may include exercise, paracetamol or NSAIDs, manual therapy, acupuncture, and multimodal rehabilitation (combined physical and psychological treatment); and (4) patients with lumbar disc herniation with radiculopathy may benefit from spinal manipulation. Ten guidelines were of high methodological quality, but updating and some methodological improvements are needed. Overall, most guidelines target nonspecific LBP and recommend education, staying active/exercise, manual therapy, and paracetamol or NSAIDs as first-line treatments. The recommendation to use paracetamol for acute LBP is challenged by recent evidence and needs to be revisited. SIGNIFICANCE: Most high-quality guidelines recommend education, staying active/exercise, manual therapy and paracetamol/NSAIDs as first-line treatments for LBP. Recommendation of paracetamol for acute LBP is challenged by recent evidence and needs updating.

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)0.123
metaresearch head score (Gemma)0.176
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.123
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1230.176
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0170.021
Bibliometrics0.0280.031
Science and technology studies0.0050.005
Scholarly communication0.0080.007
Open science0.0070.008
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0250.005

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 head0.098
GPT teacher head0.455
Teacher spread0.357 · 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 designSystematic review
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

Citations432
Published2016
Admission routes3
Has abstractyes

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