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Record W4220907675 · doi:10.1097/pr9.0000000000000997

Low back pain definitions: effect on patient inclusion and clinical profiles

2022· article· en· W4220907675 on OpenAlexaffabout

Bibliographic record

VenuePAIN Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCentre Hospitalier de l’Université de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité du Québec en Abitibi-TémiscamingueUniversité LavalUniversité de SherbrookeConcordia UniversityCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsLow back painCohortOutcome (game theory)Inclusion (mineral)Sample (material)Cohort study

Abstract

fetched live from OpenAlex

Introduction: Numerous definitions of acute low back pain (aLBP) exist. The use of different definitions results in variability in reported prevalence or incidence, conflicting data regarding factors associated with the transition to chronic LBP (cLBP), and hampers comparability among studies. Objective: Here, we compare the impact of 3 aLBP definitions on the number of aLBP cases and participants' characteristics and explore the distribution of participants across definitions. Methods: A sample of 1264 participants from the Quebec Low Back Pain Study was included. Three definitions of aLBP were used: (1) not meeting the National Institutes of Health (NIH) cLBP definition ("nonchronic"), (2) pain beginning <3 months ago ("acute"), and (3) pain beginning <3 months with a preceding LBP-free period ("new episode"). Results: There were 847, 842, and 489 aLBP cases meeting the criteria for the 3 definitions, respectively. Participants included in the "nonchronic" had lower pain interference, greater physical function scores, and fewer participants reporting >5 years of pain than in the other definitions. Half the participants meeting the "acute" definition and one-third of participants meeting the "new episode" definition were also classified as cLBP based on the NIH definition. Conclusions: Our results highlight the importance of the definition used for aLBP. Different definitions influence the sample size and clinical profiles (group's characteristics). We recommended that cohort studies examining the transition from aLBP to cLBP ensure that the definitions selected are mutually exclusive (ie, participants included [aLBP] differ from the expected outcome [cLBP]).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.472
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.297
Teacher spread0.280 · 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 teacher head, 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".

Quick stats

Citations16
Published2022
Admission routes2
Has abstractyes

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