Sensitivity and specificity of algorithms for the identification of nonspecific low back pain in medico-administrative databases
Bibliographic record
Abstract
ABSTRACT: Identifying nonspecific low back pain (LBP) in medico-administrative databases is a major challenge because of the number and heterogeneity of existing diagnostic codes and the absence of standard definitions to use as reference. The objective of this study was to evaluate the sensitivity and specificity of algorithms for the identification of nonspecific LBP from medico-administrative data using self-report information as the reference standard. Self-report data came from the PROspective Québec Study on Work and Health , a 24-year prospective cohort study of white-collar workers. All diagnostic codes that could be associated with nonspecific LBP were identified from the International Classification of Diseases, Ninth and Tenth Revisions ( ICD-9 and ICD-10 ) in physician and hospital claims. Seven algorithms for identifying nonspecific LBP were built and compared with self-report information. Sensitivity analyses were also conducted using more stringent definitions of LBP. There were 5980 study participants with (n = 2847) and without (n = 3133) LBP included in the analyses. An algorithm that included at least 1 diagnostic code for nonspecific LBP was best to identify cases of LBP in medico-administrative data with sensitivity varying between 8.9% (95% confidence interval [CI] 7.9-10.0) for a 1-year window and 21.5% (95% CI 20.0-23.0) for a 3-year window. Specificity varied from 97.1% (95% CI 96.5-97.7) for a 1-year window to 90.4% (95% CI 89.4-91.5) for a 3-year window. The low sensitivity we found reveals that the identification of nonspecific cases of LBP in administrative data is limited, possibly due to the lack of traditional medical consultation.
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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 | 0.064 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".