Patients’ perception of error during craniotomy for brain tumour and their attitudes towards pre-operative discussion of error: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: Medical error can result in significant morbidity and even mortality. Public and media attention remains focussed on its incidence and causes. Appreciation of patient perception of medical error in the neurosurgical setting is limited. This study investigated patients' perceptions of potential medical error during craniotomy for brain tumour and whether this influenced their decision to consent. MATERIALS AND METHODS: This study utilised qualitative research methodology. Thirty-five patients who had undergone craniotomy for brain tumour were interviewed using a semi-structured questionnaire. Interviews were transcribed and subjected to thematic analysis. RESULTS: Analysis revealed seven overarching themes: (i) views on what constituted medical error were well formed; (ii) to err is human; (iii) protocols exist to prevent error; (iv) trust in one's surgeon is important; (v) patients' belief that they can influence the likelihood of error was variable; (vi) concern with treating the disease trumps worry over possible errors; and (vii) the usefulness of discussing potential error was variable. CONCLUSIONS: Patients had a good understanding of medical error and it's potential causes. The usefulness of pre-operative, pre-consent discussion of error was varied. It may empower clinicians and patients to talk about such issues, though this should avoid exacerbating a patient's anxiety.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".