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Record W1946548095 · doi:10.1186/s12891-015-0669-0

Chronic low back pain clinical outcomes present higher associations with the STarT Back Screening Tool than with physiologic measures: a 12-month cohort study

2015· article· en· W1946548095 on OpenAlexafffund
Isabelle Pagé, Jacques Abboud, Julie O ̓Shaughnessy, Louis Laurencelle, Martin Descarreaux

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2015
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersFonds de Recherche du Québec - SantéInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du TravailFondation Chiropratique du Québec
KeywordsMedicineLow back painPhysical therapyPhysical medicine and rehabilitationOswestry Disability IndexBack painSports medicineRating scaleRehabilitationAlternative medicinePsychologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Stratification strategies based on identifying patient's prognosis in order to guide patient care constitute one of the most prominent and recent approach in low back pain research. The STarT Back Screening Tool (SBST) although promising, has not been studied in patients with chronic low back pain (cLBP). Considering how challenging it is to translate research into practice, the value of integrating a new tool should be thoroughly assessed. The purpose was therefore to assess associations between the short- and long-terms clinical status and two types of variables, physiologic measures and the SBST, in participants with cLBP. The ability of both types of variables to discriminate between participants with and without higher levels of disability, pain, fear of movement and patient's global impression of change was also investigated. METHODS: Fifty-three volunteers with cLBP participated in an initial evaluation and follow-ups at 2-, 4-, 6- and 12-month. Physiologic measures (maximal voluntary contraction, maximal endurance and muscle activity evaluated during prone and lateral isometric tasks) and the SBST were assessed at baseline. Disability (Oswestry Disability Index, ODI), pain intensity (101-point Numerical Rating Scale, NRS), fear of movement (Tampa Scale for Kinesiophobia, TSK) and patient's global impression of change (7-point scale, PGIC) were evaluated at baseline and at each follow-up. Aside the use of correlation analyses to assess potential associations; ROC curves were performed to evaluate the discriminative ability of physiologic measures and the SBST. RESULTS: The SBST allowed for the identification of participants presenting higher levels of disability (ODI ≥24 %), pain (NRS ≥37 %) or fear of movement (TSK ≥41/68) over a 12-month period (AUC = 0.71 to 0.84, ps < 0.05). The SBST score was also correlated with disability at each follow-up (τ = 0.22 to 0.33, ps < 0.05) and with pain intensity and fear of movement at follow-ups. Among physiologic measures, only maximal voluntary contraction was correlated to disability, pain intensity or fear of movement during the follow-up (|τ| = 0.26 to 0.32, ps < 0.05) and none was able to identify participants presenting higher levels of outcomes (AUC ps > 0.05). CONCLUSION: Physiologic measures obtained during prone and lateral tests have limited associations with the clinical status over a 12-month period in patients with nonspecific chronic low back pain. On the other hand, the STarT Back Screening Tool is useful for the identification of patients who will present higher levels of disability, pain intensity and fear of movement over a year. TRIAL REGISTRATION: Clinicaltrials.gov NCT02226692.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.045
GPT teacher head0.319
Teacher spread0.274 · 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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Citations28
Published2015
Admission routes2
Has abstractyes

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