How oncologists communicate information to women with recurrent ovarian cancer in the context of treatment decision making in the medical encounter
Bibliographic record
Abstract
BACKGROUND: Women with recurrent ovarian cancer depend on their physicians to provide them with information about their diagnosis and available treatment options if they wish to participate in the process of choosing the treatment. There is no information on how oncologists give information to women during the physician-patient encounter at the time the disease recurs. OBJECTIVES: To explore from the oncologists' perspective (i) the extent to which oncologists provide their own patients who are experiencing their first recurrence of ovarian cancer with the same information about management options, and (ii) any explicit or implicit criteria they use to decide whether and how to tailor the information to individual patients. METHODS: We adopted a qualitative, exploratory descriptive approach to begin to understand oncologists' perspectives on how they gave information to patients within the context of their clinical practice. Individual interviews were used to identify themes related to the study objectives. RESULTS: Fifteen gynaecologic and five medical oncologists participated. Theme 1 describes the extent to which oncologists give information to their patients in the same way or in different ways. This section describes how the same oncologist may modify the depth of information transfer based on several factors. Theme 2 focuses on the factors that influence what information is given. For example, the amount and type of information given is based on the oncologist's on-going assessment of how the patient is assimilating the information shared during the medical encounter, the oncologists' perception of their relationship with the patient and the oncologist's assessment of what role they should take in decision making. Theme 3 involves the factors that influenced how information is given. For example, the information shared may vary based on the oncologist's perception of the patient's vitality, the patient's comprehension of the information, the patient's emotional well-being. In addition, the oncologist may make the information relevant for the patient by using analogies. Different types of information may be shared based on the oncologist's perception of patient- or family-initiated question. The information relay may be curtailed based on competing demands for the oncologist. DISCUSSION AND CONCLUSIONS: Oncologists provide women with information on their disease status, their treatment options and the side effects of treatment. The oncologists use perceptions to determine what information and how to provide information. The question this paper raises is whether the oncologist's perceptions reflect the individual patient's information and decision-making needs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".