Correlations between pain extent and clinical features in chronic low back pain and chronic neck pain patients
Bibliographic record
Abstract
BACKGROUND: The extent of pain reported with pain drawings (PD) by chronic low back pain (CLBP) and chronic neck pain (CNP) patients may correlate and even predict some clinical features such as pain related disability, psychological distress and pain intensity. Due to the paucity of studies on this topic and to the heterogeneity of methods used for pain extent estimation, data on these correlations are lacking and often conflicting. The aim of this study was to investigate the correlations between pain extent and clinical features in CLBP and CNP patients. METHODS: Fifty-one CLBP (20 men, 31 women), and fifty-six and CNP (15 men, 41 women) patients participated. Each patient shaded a PD using a stylus pen on an iPad® (Fig 1). A custom designed software was used to quantify the pain extent, expressed as the number of pixels coloured inside the body chart perimeter. Data on clinical variables were then collected as follows: pain-related disability using the Roland and Morris Disability Questionnaire and the Neck Disability Index (NDI) for the CLBP and the CNP patients respectively, psychological distress using the Kessler Psychological Distress Scale (K-10), and pain severity using the visual analog scale (VAS). RESULTS: Pearson correlation coefficient within CNP group showed that pain extent was positively associated to pain-related disability (r:0.404, p=0.002) and pain severity (r:0.375, p=0.004). No significant correlations were found between pain extent and clinical variables within CLBP group (Table 1). DISCUSSION: It’s reasonable to expect that patients referring widespread pain or pain in multiple spots report also more severe pain. The same reasoning could be made about pain related disability, where higher pain extent is likely to reduce more the ability to carry out activities of daily living. These hypothesis were confirmed only in CNP patients but not in CLBP ones where any correlation was observed between pain extent and clinical features. CONCLUSIONS: These findings provide a better understanding of the clinical relevance of pain extent in CLBP and CNP patients. Future investigation should establish whether the clinical relevance of pain extent depends on the pain nature and/or on its anatomical distribution.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".