Bibliographic record
Abstract
ObjectivesThe aim of this review is to provide fundamental data for low back pain scales which can be used in clinical trial.MethodsWe investigated the latest studies on chronic low back pain via PubMed. And we also investigated domestic studies through “http://oasis.kiom.re.kr”. 95 research pa-pers were analyzed. Scales were classified into pain scale, function scale, generic health status scale and psychological scale.Results1) According to foreign clinical studies, Visual Analog Scale (VAS) and Numerical Rating Scale (NRS) were used 18 times as pain scale. Oswestry Disability Index (ODI) was used 20 times as function scale, Roland-Morris Disability Questionnaire (RMDQ) was 17, and Hannover Functional Ability Questionnaire (HFAQ) was used 3 times. 36-item Short Form Health Survey (SF-36) was used 13 times as generic health status scale, Euroqol-5 Dimentions Questionnaire (EQ-5D) was 11, and 12-item Short Form Health Survey (SF-12) was used 3 times. Fear-Avoidance Beliefs Questionnaire (FABQ) was used 9 times as psy-chological scale, Pain Catastrophizing Scale (PCS) and Tampa Scale for Kinesiophobia (TSK-R) both were used 3 times. 2) According to domestic clinical studies, VAS was used 37 times as pain scale, NRS was 11, and Short Form McGill Pain Questionnaire (SF-MPQ) was used 6 times. ODI was used 30 times as function scale, RMDQ was 2 times only. SF-36 was used once as generic health status scale and Beck’s Depression Inventory (BDI) was used 3 times as psychological scale.ConclusionsWe recommend VAS or NRS as a measure to evaluate pain, and ODI as a measure to evaluate functional disability. And we also recommend SF-36 or SF-12 and EQ-5D as a measure to evaluate generic health status. Finally, we recommend FABQ for use in measuring psychological scale. (J Korean Med Rehab 2013;23(4):95-115)
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".