Low back pain definitions: effect on patient inclusion and clinical profiles
Bibliographic record
Abstract
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]).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.277 | 0.471 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".