PL02.4.A SHARED DECISION-MAKING IN NEUROSURGICAL CONSULTATIONS WITH NEWLY DIAGNOSED BRAIN TUMOR PATIENTS: A THEMATIC ANALYSIS OF PATIENT INVOLVEMENT
Bibliographic record
Abstract
Abstract BACKGROUND In shared decision-making (SDM) the patient serves as the expert on his or her own life and the health care professional serves as the medical expert. However, many SDM instruments that employ observer coding schemes primarily focus on information provision, and less on patient involvement. The items that are included on patient involvement are as follows: 1) introducing SDM to the patient, 2) providing personalized information and 3) discussing patient’s views, values and situation. These items may mark the presence of patient involvement and will be taken as a starting-point for this study, yet they do not provide information on how these items are discussed as part of the SDM process. The aim of this study is to illustrate the involvement of newly diagnosed brain tumor patients in SDM by analysing the decision-making process during neurosurgical consultations and identifying sub-themes related to patient involvement. MATERIAL AND METHODS This study employed a codebook thematic analysis to identify the sub-themes concerning aspects of patient involvement in the neurosurgical decision-making process. The first consultation between newly diagnosed glioma or meningioma patients and their neurosurgeons were audio-recorded, transcribed and coded. The study followed a recursive design continuing with analysis until data saturation was reached. RESULTS Saturation was reached after analysing 11 consultations. We identified 13 sub-themes within the three main themes, and we will support our findings with direct quotes from the consultations during the conference presentation. Theme 1 was comprised of the sub-themes inviting the patients to participate, presenting treatment options, inquiring about symptoms and daily life, asking patients about treatment preference and room for questions. Questions about patients’ personal circumstances are often asked near the end of a consultation, when the decision regarding treatment has already been made. Theme 2 was comprised of the sub-themes personalizing information, personalizing aspects of treatment and personalizing treatment recommendations. There are examples of neurosurgeons integrating information provided by the patient to personalize the decision-making process. Theme 3 was comprised of the sub-themes interference of disease, daily life, family and requested information. These sub-themes show what patients (spontaneously) share about their lives. CONCLUSION We illustrated how brain tumor patients contribute to the SDM process, what information patients and their decision-partners provide and how this information is integrated in the decision-making process during neurosurgical consultations. Our findings may provide handholds to further improve patient involvement and personalization of the decision-making 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 | 0.033 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".