Information gaps for patients requiring craniotomy for benign brain lesion: a qualitative study
Bibliographic record
Abstract
Doctor-patient communication in the setting of a life-threatening illness poses considerable challenges. This study aimed to determine the information needs of a subset of neurosurgical patients. Qualitative case study methodology was used. Twenty-five semi-structured interviews were conducted with ambulatory adult patients who had undergone surgery for a benign brain tumor, arteriovenous malformation, or unruptured aneurysm. Interviews were digitally audio recorded and transcribed, and the data subjected to thematic analysis. Six overarching themes emerged from the data: (1) the amount of information patients want varies; (2) the type of information needed is not limited to information about treatment options and risks; (3) patients engage in independent information seeking for a variety of reasons; (4) patients consider compassion from their surgeon as important; (5) direct communication with the surgeon post-operatively is very important; and (6) patients' information needs are greatest post-operatively. Many patients felt that the amount and quality of information they received was not sufficient, particularly regarding post-operative recovery and long-term life issues, leading many to do their own research. The findings from this study emphasize the need for improved communication with patients so they can participate meaningfully in choices about their treatment, give a truly informed consent, and effectively participate in their own recovery.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".