Disability Self-evaluation for Low Back Pain in COVID-19 Pandemic
Bibliographic record
Abstract
ABSTRACTIntroduction: Low back pain (LBP) interferes with daily activities, which is why monitoring offunctional disability is important. Non-urgent hospital visits are reduced due to the COVID-19 pandemic.Functional disability questionnaires serve as an alternative fo r patients to self-monitor their condition.Methods: This case-based study aimed to compare the Quebec Back Pain Disability Scale (QBPDS) withthe Oswestry Disability Index (ODI) on their responsiveness in assessing functional disability of patientswith LBP. Four databases (PubMed, Scopus, Cochrane, and Embase) were searched for literature. Twoeligible studies were included in this report. The studies were assessed using the Centre for Evidence-Based Medicine critical appraisal tool for diagnostic studies. Data collected on the responsiveness ofODI and QBPDS were measured using the area under the curve (AUC) of a receiver operating curve(ROC), sensitivity, and specificity.Result: Both studies reported higher AUC values for ODI than QBPDS. One study reported highersensitivity in ODI and identical specificity values for both ODI and QBPDS. QBPDS has comparableresponsiveness to ODI in assessing functional disability of pat ients with LBP.Conclusion: Therefore, patients with low back pain can self-monitor their condition with QPBDS, as itis comparable to ODI and suitable for self-monitor during the C OVID-19 pandemicKeywords: assessment, disability evaluation, low back pain, musculoskeletal pain, surveys andquestionnaires
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".