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Record W2600568825

Using the Quebec Task Force Classification to subgroup low back pain patients in primary care: an analysis of longitudinal clinical data from chiropractic and general practice

2016· article· en· W2600568825 on OpenAlexaboutno aff
Lisbeth Hartvigsen, Lise Hestbæk, Charlotte Leboeuf‐Yde, Werner Vach, Alice Kongsted

Bibliographic record

VenueUniversity of Southern Denmark Research Portal (University of Southern Denmark) · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsChiropracticPrimary careTask forceMedicineTask (project management)Subgroup analysisLow back painClinical PracticePhysical medicine and rehabilitationPhysical therapyBack painAlternative medicineFamily medicinePolitical scienceInternal medicineEngineeringPathology
DOInot available

Abstract

fetched live from OpenAlex

Background Low back pain (LBP) patients with related leg pain and signs of nerve root involvement (NRI) are considered to have a worse prognosis than patients with LBP alone. However, knowledge is limited about the importance of distinguishing between leg pain above or below the knee and leg pain with and without NRI. The objectives of this study were 1) to investigate whether patients in the four QTF categories (LPB alone, LBP + leg pain above knee, LBP + leg pain below knee, and LBP + NRI) differ on baseline characteristics; 2) to investigate the relationship between QTF categories and global perceived effect (GPE) and activity limitation after 2 weeks, 3 months, and 1 year as well as one-year-trajectories of LBP intensity; including whether there is a hierarchy of the four groups in terms of severity in outcome measures, and to what extent QTF categories predict these outcomes; and 3) to describe whether this relationship is similar chiropractic practice (CP) and general practice (GP). Method This is a prospective observational cohort study of adult patients seeking care for LBP in CP or GP. Patients completed an extensive baseline questionnaire and were classified into the four QTF categories by the practitioner. Associations between QTF categories and outcomes were tested using univariate regression models and models adjusted for demographic factors . Predictive capacity was quantified in terms of R-squared and Area Under the ROC Curve (AUC). Results The study comprised 1271 (947 from CP and 324 from GP) patients. Compared with patients with LBP alone, patients with leg pain and patients with NRI were more severely affected across baseline characteristics and outcome measures in both CP and GP. QTF categories were associated with activity limitation at all follow-up time-points in both CP and GP (p<0.001). Patients with LBP alone had the least activity limitation at all time points and LBP + NRI had the most activity limitation with the exception of GP patients at 2 weeks. In both cohorts, QTF-categories were associated with 2 of 5 trajectories of LBP intensity (p< 0.05) and with GPE at two weeks (p< 0.05) but not 3 months and 1 year. Nearly twice as many patients with LBP alone compared with patients with LBP + NRI were improved at 2 weeks in both cohorts. None of the outcomes were accurately predicted by the QTF (R2 range: 0.05-0.14); AUC range: 0.53-0.65). Conclusions The QTF classification differentiated between distinctly different patient subgroups. Local LBP was on all parameters the least severe condition. Although less clear-cut, distribution of leg pain and clinical signs of NRI differentiated between patient groups with different clinical presentation and course; generally with increasing severity across categories from LBP alone, over LBP + above knee and LBP + below knee to LBP + NRI.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.346
Teacher spread0.266 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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