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Record W4366075637 · doi:10.1093/ptj/81.10.1641

Philadelphia Panel Evidence-Based Clinical Practice Guidelines on Selected Rehabilitation Interventions for Low Back Pain

2001· article· en· W4366075637 on OpenAlexaff
John P. Albright, Richard M. Allman, Richard Paul Bonfiglio, Alicia Conill, Bruce H. Dobkin, Andrew A. Guccione, Scott Hasson, Randolph Russo, Paul Shekelle, Jeffrey Susman, Lucie Brosseau, Peter Tugwell, George A. Wells, MSc Vivian A Robinson, Ian D. Graham, MSc Beverley J Shea, Jessie McGowan, Joan Peterson, Michel Tousignant, MSc Lucie Poulin, Hélène Corriveau, B. Morin, Lucie Pelland, MHA Lucie Laferrière, Lynn Casimiro, Louis E. Tremblay

Bibliographic record

VenuePhysical Therapy · 2001
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsOttawa HospitalCentre for Global Health ResearchHealth CanadaUniversity of OttawaMinistry of Health and Long Term Care
Fundersnot available
KeywordsData extractionMedicinePsychological interventionObservational studyRandomized controlled trialRehabilitationGrading (engineering)Physical therapyEvidence-based medicineLow back painMEDLINEFamily medicineAlternative medicineNursingSurgery

Abstract

fetched live from OpenAlex

Introduction. A structured and rigorous methodology was developed for the formulation of evidence-based clinical practice guidelines (EBCPGs), then was used to develop EBCPGs for selected rehabilitation interventions for the management of low back pain. Methods. Evidence from randomized controlled trials (RCTs) and observational studies was identified and synthesized using methods defined by the Cochrane Collaboration that minimize bias by using a systematic approach to literature search, study selection, data extraction, and data synthesis. Meta-analysis was conducted where possible. The strength of evidence was graded as level I for RCTs or level II for nonrandomized studies. Developing Recommendations. An expert panel was formed by inviting stakeholder professional organizations to nominate a representative. This panel developed a set of criteria for grading the strength of both the evidence and the recommendation. The panel decided that evidence of clinically important benefit (defined as 15% greater relative to a control based on panel expertise and empiric results) in patient-important outcomes was required for a recommendation. Statistical significance was also required, but was insufficient alone. Patient-important outcomes were decided by consensus as being pain, function, patient global assessment, quality of life, and return to work, providing that these outcomes were assessed with a scale for which measurement reliability and validity have been established. Validating the Recommendations. A feedback survey questionnaire was sent to 324 practitioners from 6 professional organizations. The response rate was 51%. Results. Four positive recommendations of clinical benefit were developed. Therapeutic exercises were found to be beneficial for chronic, subacute, and postsurgery low back pain. Continuation of normal activities was the only intervention with beneficial effects for acute low back pain. These recommendations were mainly in agreement with previous EBCPGs, although some were not covered by other EBCPGs. There was wide agreement with these recommendations from practitioners (greater than 85%). For several interventions and indications (eg, thermotherapy, therapeutic ultrasound, massage, electrical stimulation), there was a lack of evidence regarding efficacy. Conclusions. This methodology of developing EBCPGs provides a structured approach to assessing the literature and developing guidelines that incorporates clinicians' feedback and is widely acceptable to practicing clinicians. Further well-designed RCTs are warranted regarding the use of several interventions for patients with low back pain where evidence was insufficient to make recommendations.

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)0.092
metaresearch head score (Gemma)0.150
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.487

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.150
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0080.014
Bibliometrics0.0210.018
Science and technology studies0.0040.003
Scholarly communication0.0080.004
Open science0.0170.008
Research integrity0.0190.016
Insufficient payload (model declined to judge)0.0110.011

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 head0.674
GPT teacher head0.616
Teacher spread0.059 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations150
Published2001
Admission routes1
Has abstractyes

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