Implementing a Pilot Geriatric Oncology Service at a National Cancer Centre in Dublin
Bibliographic record
Abstract
Abstract Background The American Society of Clinical Oncology and International Society of Geriatric Oncology recommend comprehensive geriatric assessment (CGA) for older adults commencing systemic anti-cancer treatments (SACTs) (1, 2). Routine CGA is lacking in cancer centres nationally. Methods A pilot, interdisciplinary geriatric-oncology (GO) clinic comprising a geriatric medicine consultant and specialist registrar, medical oncology fellow, physiotherapist and occupational therapist was established Dec 2023. New oncology patient referrals were screened applying the G8 tool; a score of ≤14 triggering referral. Polypharmacy was defined as ≥5 regular medications. Frailty was assessed using Clinical Frailty Scale (CFS) and cognition using Montreal Cognitive Assessment (MoCA). Results To date, 30 patients have attended the clinic; mean age 77.2 (SD5) years (range 68-85); 73% female, 50% with a CFS score of ≥4. Sixty-percent (n=18) were due to commence chemotherapy, the remainder hormonal or targeted treatment. Polypharmacy was identified in 66.6% (n=20), with 13.3% (n=4) having an anti-cholinergic burden score of ≥3, MoCAs were completed in 60% (n=18), of whom 77.8% (n=14) had an abnormal result (<26/30 MoCA or <18/22 MoCA blind). Twenty-six percent (n=8) reported ≥1 fall in the preceding six-months. Deprescribing interventions were completed in 40% (n=12); 58.3% (n=7) had ≥2 medications stopped. Medications most commonly deprescribed included opioids (n=2), antihypertensives (n=2), statins (n=2). Ambulatory blood pressure monitors were arranged for 33.3% (n=10) and DEXAs for 40% (n=12) and appropriately actioned. Follow up was arranged in the Falls and Syncope Unit for 7%, day hospital 10%, geriatric outpatients 10%, group psycho-oncology sessions 20%, specialist cancer rehabilitation physiotherapy service 17%. Parkinsons disease and dementia were diagnosed in 1 and 1 patients, respectively. Each patient had a detailed assessment and plan forwarded to their treating oncologist. Conclusion The development of a dedicated GO service is in line with international standards. By undergoing CGA, geriatric syndromes amenable to intervention were identified in patients.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".