Impact of Clinician-Patient Communication in Cancer using a Patient Communication Profile: A Randomized, Controlled Study
Bibliographic record
Abstract
Background/objectives: Effective communication is critical to optimizing cancer care, influencing treatment plans and outcomes. Studies consistently demonstrate that clear, well-structured communication with cancer patients significantly enhances therapeutic results and plays a central role in meeting patient communication needs and improving care quality. Methods: This randomised, controlled, parallel, study evaluated the impact of the Patient Communication Profile (PCP) and quality of life (QoL) measures through the use of the McGill QoL instrument, on cancer patient outcomes. The PCP was completed by participants at baseline and 2 follow-up visits over 4 weeks and the McGill QoL instrument was completed at baseline and second follow-up visit. Questionnaires were made available to oncologists for the study group while these were blind for oncologists of the control group. Results: 105 patients were randomized (2:1) into control (N=41) and study groups (N=64). At baseline, no differences were observed between groups, while over the follow-up period, a better understanding and higher level of satisfaction with their encounters, due to a more comprehensive acquisition of information, was reported in the study group vs. control (p<0.05). The periodical and individualized feedback on need for information and related issues significantly contributed to the improvement in QoL (assessed using the McGill questionnaire) in the study group (p<0.05). Furthermore, significant improvements in mean PCP domain scores in the study group were also observed (p<0.05). Conclusion: Our findings show that integrating routine assessments of patients' information needs with individualized feedback to clinicians before encounters enhances mutual understanding. This approach improves patient QoL and optimizes treatment adherence in 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".