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Record W2201056158 · doi:10.3138/ptc.2014-58

Mechanical Low Back Pain: Secular Trend and Intervention Topics of Randomized Controlled Trials

2015· article· en· W2201056158 on OpenAlexvenueno aff
Greta Castellini, Silvia Gianola, Giuseppe Banfi, Stefanos Bonovas, Lorenzo Moja

Bibliographic record

VenuePhysiotherapy Canada · 2015
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionRandomized controlled trialPhysical therapyRehabilitationLow back painGynecologyAlternative medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the number of published randomized controlled trials (RCTs) focusing on mechanical low back pain (MLBP) rehabilitation, the secular (i.e., long-term) trend, and the distribution of interventions studied. METHODS: All included RCTs were extracted from all Cochrane systematic reviews focusing on rehabilitation therapies for MLBP, and two independent reviewers screened and analyzed the information on interventions. RESULTS: After removal of duplicates, the data set consisted of 196 RCTs published between 1961 and 2010. The number of RCTs published increased consistently over time: 2 trials (1% of the total) were published in 1961-1970, 10 (5%) in 1971-1980, 41 (21%) in 1981-1990, 68 (35%) in 1991-2000, and 75 (38%) in 2001-2010. The intervention of interest in the majority of RCTs was exercise therapy (115/399; 29%), followed by spinal manipulation therapies (60/399; 15%). CONCLUSION: The number of RCTs focusing on MLBP has risen over time; of all interventions studied, exercise therapy has attracted the most research interest.

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)0.369
metaresearch head score (Gemma)0.702
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.631
Threshold uncertainty score0.778

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3690.702
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0220.031
Science and technology studies0.0010.004
Scholarly communication0.0100.011
Open science0.0030.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.016
GPT teacher head0.306
Teacher spread0.291 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
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

Citations15
Published2015
Admission routes1
Has abstractyes

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