Bibliographic record
Abstract
INTRODUCTION Studies report that after lower-limb amputation, patients have high levels of pain in the form of chronic low back pain, residual limb pain, and phantom pain leading to restrictions in functional activity1. Patients with high levels of pain and disability may develop avoidance behaviors. Vlaeyen et al. presents the fear-avoidance model, which suggests that after an injury there are two pathways a patient will take based on their interpretation of acute pain 2. Pain that is perceived as non-threatening leads to the patient’s recovery and return to normal activities of daily life. Pain that is perceived as threatening, or pain catastrophizing, causes anxiety and induces mobility apprehension which leads to avoidance behaviors. Avoidance behaviors may then lead to greater pain, depression, and disability3 . Factors described in the literature that are related to mobility apprehension were measured in a sample of lower limb amputees. The purpose was to determine which of pain intensity, interference, and catastrophizing lead to increased mobility apprehension. Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32040/24454 How to cite: Mathis S.L. FACTORS ASSOCIATED WITH MOBILITY APPREHENSION IN AMPUTEES. CANADIAN PROSTHETICS & ORTHOTICS JOURNAL, VOLUME 1, ISSUE 2, 2018; ABSTRACT, ORAL PRESENTATION AT THE AOPA’S 101ST NATIONAL ASSEMBLY, SEPT. 26-29, VANCOUVER, CANADA, 2018. DOI: https://doi.org/10.33137/cpoj.v1i2.32040 Abstracts were Peer-reviewed by the American Orthotic Prosthetic Association (AOPA) 101st National Assembly Scientific Committee. http://www.aopanet.org/
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".