It's a choice to move forward: women's perceptions about treatment decision making in recurrent ovarian cancer
Bibliographic record
Abstract
OBJECTIVE: This research explores the treatment decision-making (TDM) experiences of women with recurrent ovarian cancer (ROC) with regard to treatment options; their understanding of risks and benefits of various treatment options; the decision-making role they want for themselves and for their oncologist; and the social context of the consultation as it pertains to the decision. METHODS: We conducted semi-structured interviews with 26 women at the time of first recurrence. Through inductive data analysis key themes were identified. RESULTS: Many women describe self-identifying the cancer recurrence fairly quickly due to new symptoms. Many feel that the goal for treating their recurrence is to control versus cure the cancer. They describe the subsequent process of diagnosis and TDM for ROC as quick and straightforward with all women accepting the oncologists' treatment recommendation. They feel that the type and number of treatment options are limited. They have a strong desire for physician continuity in their care. Participants feel that their doctor's recommendations as well as their previous experience with ovarian cancer are strong factors influencing their current TDM process. CONCLUSIONS: Shared decision making is based on a simultaneous participation of both the physician and patient in TDM. When faced with ROC, women feel that their doctor's recommendation and their past experience with treatment and TDM are prominent factors influencing the current TDM process.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".