Are Older Cancer Patients Being Referred to Oncologists? A Mail Questionnaire of Ontario Primary Care Practitioners to Evaluate Their Referral Patterns
Bibliographic record
Abstract
PURPOSE: Understanding why older patients are frequently underrepresented in cancer services use and clinical research may help to increase their participation in clinical trials and eventually result in better cancer care for this vulnerable population. METHODS: To identify potential barriers that may prevent older cancer patients from being referred from a primary care physician (PCP) to an oncology specialist, a self-administered questionnaire was mailed to 9,312 PCPs throughout Ontario. RESULTS: With a one-time mailing, 2,240 questionnaires were returned (response rate, 24%) of which 2,089 (93%) were assessable. Although 86% of respondents would refer most older patients with early-stage, potentially curable cancers to oncologists, only 65% would refer those with advanced-stage, potentially incurable cancers. The factors that most influence referral decisions of PCPs are patient's desire to be referred (69%), type (54%) and stage (49%) of cancer, and severity of cancer symptoms (49%). Other factors including age do not seem to influence the referral decision. Approximately 9% of respondents found it difficult to refer older cancer patients to oncology specialists, with the most commonly cited barriers being the length of waiting lists, mandatory tissue diagnosis before referral, and the belief that oncologists seldom relate to PCPs. CONCLUSION: Most PCPs stated that they would refer all elderly patients with cancer to oncologists and that referral decisions were based mainly on patients' wishes. Continued efforts are needed to overcome barriers in the referral process and to understand the perspectives of elderly patients to enhance their cancer care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".