Bibliographic record
Abstract
Abstract: Purpose: To determine the outcome measures practice patterns in the neck pain management of various health disciplines. Methods: A survey of 381 clinicians treating patients with neck pain was conducted. Results: Respondents were more commonly male (54%) and either chiropractors (44%) or physiotherapists (32%). The survey was international (24 countries with Canada having the largest response (44%)). The most common assessment was a single-item pain assessment (numeric or visual analog) used by 75 % of respondents. Respondents sometimes or routinely used the Neck Disability Index (49%), the Patient Specific Functional Scale (28%), and the Disabilities of the Arm, Shoulder and Hand (32%). Work status was recorded in terms of time lost by more than 50 % of respondents, but standardized measures of work limitations or functional capacity testing were rarely used. The majority of respondents never used fear of movement, psychological distress, quality of life, participation measures, or global ratings of change (< 10 % routinely use). Use of impairment measurers was prevalent, but the type selected was variable. Quantitative sensory testing was used sometimes or routinely by 53 % of respondents, whereas 26 % never used it. Ratings of segmental joint mobility were commonly used to assess motion (44 % routinely use), whereas 66 % of respondents never used
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.638 | 0.437 |
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".