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Record W7099871956

and ICON

2016· article· en· W7099871956 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicMedicine and Dermatology Studies History
Canadian institutionsnot available
Fundersnot available
KeywordsNeck painVisual analogue scaleIconQuantitative sensory testingScale (ratio)Quality of life (healthcare)Test (biology)
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score0.127

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.253
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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