Poster (Clinical/Best Practice Implementation) ID 1998248
Bibliographic record
Abstract
Background It is essential that individuals with spinal cord injury set their goals prior to their peripheral nerve transfer (PNT) surgery. Goal setting is a significant factor in pre-operative planning and is one way in which we can track performance and outcomes for these patients. Objectives To describe goal types identified by patients managed in the PNT-SCI rehab program at Lyndhurst-UHN. Cite and report the COPM outcomes in the domains of Self-care and Productivity. Methods A retrospective case series was conducted (n=14), charts were reviewed for type of goals and patient’s perception of goal attainment using COPM for a case series of three patients with tetraplegia who received PNT-SCI surgery and comprehensive rehabilitation. The changes in COPM are reported from baseline to 12 months post-surgery. Each patient identified three goals pre-surgery; their goals and the COPM were used to measure change over time. Results Ninety two percent of the identified goals were in the area of Self Care, and 8% were in the Productivity areas. Two of the patients who received PNT-SCI rehabilitation had an increase of 1 on the COPM, while one individual regressed by 2 points. That individual did not receive comprehensive rehabilitation. Conclusion It is known that recovery after PNT-SCI surgery can take 24 or more months. We reported COPM change scores at 12 months post surgery. The MCID is two points for COPM, ideally over 24 months we will see MCID of three or higher.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.848 | 0.772 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".