Factors influencing cancer specialists' decision to collaborate with geriatricians in treating older cancer patients
Bibliographic record
Abstract
BACKGROUND: the collaboration between geriatricians and cancer specialists holds significant potential for improving care outcomes for older cancer patients. The realisation of this collaboration partly depends on cancer specialists involving geriatricians in caring for their older patients. Yet only a few studies have focused on understanding the reasons for cancer specialists' choice to involve or not involve geriatricians in this care. OBJECTIVE: this study shed some light on the challenges of collaboration between geriatricians and cancer specialists. It describes the case of a hospital that established a clinic staffed by geriatricians to assist cancer treatment teams. The focus of this article is to identify and explain the patterns of referrals of cancer specialists to this clinic. RESULTS: our study suggests that the referral practices of cancer specialists are considerably influenced by their specialty. The cancer specialists who find more applied value from geriatric assessments tend to refer their patients to geriatricians. Medical oncology is the sub-specialty that struggles the most in practically using information from the assessments to adjust their treatment. Cancer specialists who regularly referred to the clinic were the ones who thought that geriatricians had a unique contribution to patient care with their assessments and also with their intervention in palliative and psychosocial care. These specialists were usually from surgery and radiation oncology. CONCLUSIONS: ageing confers an increased risk of developing cancer. Providing adequate care to older cancer patients is still a challenge. Our study opens the 'black box' of collaboration between two important groups of professionals who may intervene in this care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".