First impressions: A prospective evaluation of patient–physician concordance and satisfaction following the initial medical oncology consultation
Bibliographic record
Abstract
BACKGROUND: An especially significant event in the patient-oncologist relationship is the initial consultation, where many complex topics-diagnosis, treatment intent, and often, prognosis-are discussed in a relatively short period of time. This study aimed to measure patients' understanding of the information discussed during their first medical oncology visit and their satisfaction with the communication from medical oncologists. METHODS: Between January and August 2021, patients without prior systemic treatment of their gastrointestinal malignancy (GI) attending the Princess Margaret Cancer Centre (PMCC) were approached within 24 h of their initial consultation to complete a paper-based questionnaire assessing understanding of their disease (diagnosis, treatment plan/intent, and prognosis) and satisfaction with the consultation. Medical oncology physicians simultaneously completed a similar questionnaire about the information discussed at the initial visit. Matched patient-physician responses were compared to assess the degree of concordance. RESULTS: A total of 184 matched patient-physician surveys were completed. The concordance rates for understanding of diagnosis, treatment plan, treatment intent, and prognosis were 92.9%, 59.2%, 66.8%, and 59.8%, respectively. After adjusting for patient and physician variables, patients who reported treatment intent to be unclear at the time of the consultation were independently associated with lower satisfaction scores (global p = 0.014). There was no statistically significant association between patient satisfaction and whether prognosis was disclosed (p = 0.08). CONCLUSION: An in-depth conversation as to what treatment intent and prognosis means is reasonable during the initial medical oncology consultation to ensure patients and caregivers have a better understanding about their cancer.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".