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Record W4385732824 · doi:10.1136/bmjopen-2022-069818

Predictive performance of the STarT Back tool for poor outcomes in patients with low back pain: protocol for a systematic review and meta-analysis

2023· review· en· W4385732824 on OpenAlexaboutno aff
Y Fang, Jie Chen, Shengmei Lin, Yangfan Cai, Lian-Hong Huang

Bibliographic record

VenueBMJ Open · 2023
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersFujian Medical UniversityNatural Science Foundation of Fujian Province
KeywordsMedicineFunnel plotPublication biasMeta-analysisMEDLINEProtocol (science)Cochrane LibraryLow back painStatisticPhysical therapyConfidence intervalAlternative medicineInternal medicineStatisticsPathology

Abstract

fetched live from OpenAlex

Introduction Subgroups for Targeted Treatment Back Tool (SBT) is a brief multiple-construct risk prediction tool for patients with low back pain (LBP). Thus far, the predictive ability of this tool has been inconsistent. Therefore, we aim to conduct a literature review on the predictive ability of the SBT to determine the outcomes of patients with LBP. The results of this review should improve the ability of the SBT to predict poor outcomes in patients with LBP. Methods and analysis Databases including PubMed, EMBASE, Cochrane Central, Web of Science, Chinese National Knowledge Infrastructure Databases, Chinese Science and Technology Journal Database, and Wanfang will be searched for studies on SBT and LBP from their inception until 31 March 2023. Longitudinal studies investigating the association between SBT subgroups and LBP outcomes, including pain, disability and quality of life, will be included. The identified studies will be independently screened for eligibility by two reviewers. A standardised sheet will be used to extract data. The Newcastle-Ottawa Scale will be used to assess the methodological quality of the included studies. Heterogeneity will be evaluated by the χ 2 test with Cochran’s Q statistic and quantified by the I 2 statistic. The results will be synthesised qualitatively and presented as pooled risk ratios or beta coefficients quantitatively. The results will also be presented using their 95% confidence limits. Publication bias will be assessed using the method proposed by Egger and by visual inspection of funnel plots. Ethics and dissemination This study is a secondary analysis of original studies that received ethics approval. Therefore, prior ethical approval is not required for this study. The findings will be submitted to relevant peer-reviewed journals for publication and presented at profession-specific conferences. Trial registration number PROSPERO registration number CRD42022309189.

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)0.079
metaresearch head score (Gemma)0.097
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.079
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.097
Meta-epidemiology (narrow)0.0070.005
Meta-epidemiology (broad)0.0210.032
Bibliometrics0.0110.009
Science and technology studies0.0030.004
Scholarly communication0.0070.006
Open science0.0050.005
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0550.007

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 head0.125
GPT teacher head0.439
Teacher spread0.314 · 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
GenreProtocol

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

Citations1
Published2023
Admission routes1
Has abstractyes

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