A Qualitative Description of Chronic Neck Pain has Implications for Outcome Assessment and Classification
Bibliographic record
Abstract
BACKGROUND: Neck pain is common, but few studies have used qualitative methods to describe it. PURPOSE: To describe the quality, distribution and behavior of neck pain. METHODS: Sixteen people (15 females; mean age = 33 years (range = 20-69)) with neck pain >3 months were interviewed using a semi-structured guide. Interview data were recorded and transcribed verbatim. Descriptive content analysis was performed by two authors. Participants then completed an electronic descriptive pain tool, placing icons (word and icon descriptors to describe quality) on anatomic diagrams to identify location of pain, and intensity ratings at each location. This data was triangulated with interviews. RESULTS: Aching pain and stiffness in the posterior neck and shoulder region were the most common pain complaints. All patients reported more than one pain quality. Associated headache was common (11/16 people); but varied in location and pain quality; 13/16 reported upper extremity symptoms. Neuropathic characteristics (burning) or sensory disturbance (numbness/tingling) occurred in some patients, but were less common. Activities that involved lifting/carrying and psychological stress were factors reported as exacerbating pain. Physical activity was valued as essential to function, but also instigated exacerbations. Concordance between the structured pain tool and interviews enhanced trustworthiness of our results. Integrating qualitative findings with a previous classification system derived a 7-axis neck pain classification: source/context, sample subgroup, distribution, duration, episode pattern, pain/symptom severity, disability/participation restriction. CONCLUSIONS: Qualitative assessment and classification should consider the multiple dimensions of neck pain.
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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 | 0.115 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".