Implementation of a Multi-Disciplinary Geriatric Oncology Clinic in Toronto, Canada
Bibliographic record
Abstract
Older adults with cancer tend to face more complex health needs than their younger counterparts. Patients > 65 years of age are recommended for comprehensive geriatric assessment (CGA) to capture and address age-related vulnerabilities. Access to geriatrics services is limited, and our baseline audit of geriatric referrals in 2019 from the cancer program revealed that only 30% of patients referred received a CGA. The aim of this study was to assess the implementation of a geriatric oncology (GO) clinic that employs CGA and determine patient outcomes. We conducted a retrospective cohort study at a single institution. Data collection included baseline characteristics, GO clinic findings and characteristics, recommendations/referrals, and emergency room (ER) visits/hospitalizations within 6 months of CGA. Descriptive statistics were used for analysis. A total of 100 patients were included, with a median (range) age of 80 (63-97) years; 70% were female, and the most common cancer type was breast (31%). Through the GO clinic, patients were seen in a timely manner, with a median of 3 weeks, compared to our historical baseline of 11 weeks. Cognitive decline (32%) and pre-treatment CGA (22%) were the most common reasons for referral, and the most common new diagnosis was cognitive impairment (65%). For pre-treatment CGA, 16 (48%) patients were deemed suitable for treatment and 10 (30%) were recommended for modified treatment; 34 (94%) referring physicians followed the recommendation. In addition, most (68%) patients received an allied health referral. One third of patients visited the ER and 30 (30%) patients were hospitalized. Overall, the GO clinic resulted in greater access to CGA in a timely manner, enhanced access to allied health, and assisted in treatment decision-making.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".