PS1 - 188 Rehabilitation Consultation: An Integrated Model for Addressing Rehabilitation Concerns in the Primary Brain Tumor Population
Bibliographic record
Abstract
Individuals with primary brain tumors experience a range of physical, cognitive and psychosocial sequelae which impact their independence, safety and quality of life. These impairments may be addressed through rehabilitation intervention. Despite acknowledgement that timely rehabilitation services over the course of the disease process is of benefit, few outpatient neuro-oncology treatment teams include a rehabilitation professional. Purpose: The aims are: (1) to describe a rehabilitation consultation model of care integrated into outpatient neuro-oncology treatment for individuals with primary brain tumors; and (2) to describe the characteristics of individuals referred for rehabilitation services. Methods: This retrospective descriptive study examined data from 200 individuals that received rehabilitation consultation from January 2015 to March 2016 at Princess Margaret Hospital, Pencer Brain Tumor Centre. Information on patient demographics, referral characteristics, and number of patient care visits was collected. Descriptive statistics were calculated. Preliminary Results: Of all patients, (n=195), the most common diagnosis is glioblastoma, 39% (n=76), and 50% are 50-69 years of age (M=55, SD=15.0). The most common reason for initial referral was decline in physical functioning, strength and balance (41%). In 77% of cases, patients were seen immediately at the time of referral. In total, 540 consultations were completed (face-to-face=230, telephone=310) with 2.78 on average (SD=4.0) per patient. Conclusion: Given the range of symptoms that individuals with primary brain tumors experience coupled with changes in functional status as the disease progresses, integrated and timely rehabilitation consultation is feasible.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".