Collaborative Rehabilitation Therapy: A Novel Telehealth Delivery and Learning System
Bibliographic record
Abstract
Over the past three decades, the Institute for Cognitive Prosthetics (ICP) has studied and reported [1] [2] [3] [4] [5] a novel approach for treating patients with cognitive deficits following traumatic brain injury (TBI) and certain non-degenerative acquired brain injuries (ABI). A research and development enterprise, ICP’s mission is to advance clinical outcomes by patient use of technology and by providing therapists with new tools that expand their ability to produce clinical outcomes. ICP subsequently established Neuro-Hope as the provider of the professional services now referred to as Collaborative Rehabilitation Therapy (CoRT). CoRT is designed to support patients with a broad range of challenges as well as their families who experience relationship disruptions following sudden brain injury in a loved one. [1] Cole, E., Petti, L., Matthews, Jr., M. & Dehdashti, P. (1994). Rapid functional improvement and generalization in a young stroke patient following computer-based cognitive prosthetic intervention. Presentation at the 1994 NIH Neural Prosthesis Workshop, October 19-21. [2] Cole, E. (2013). Patient-centered design of cognitive assistive technology for traumatic brain injury telerehabilitation. Toronto: Morgan & Claypool. [3] Cole, E. & Starr, L. M. (2021). Collaboration therapy: telehealth principles and case studies. Thomas Jefferson University School of Continuing and Professional Studies Faculty Papers. Paper 9: https://jdc.jefferson.edu/jscpsfp/9. [4] Cole, E. (1999). Cognitive Prosthetics: An Overview to a Method of Treatment. NeuroRehabilitation, 12(1):39–51. https://DOI.org/10.3233/NRE-1999-12105. [5] Cole, E. (2021). Outcomes of a technology-enhanced, patient-centered cognitive rehabilitation therapy program delivered to the patient’s natural environment via telehealth 9 years post injury. Institute for Cognitive Prosthetics, Working Paper 2021-3.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".