e-Consent: approaching surgical consent with mobile technology
Bibliographic record
Abstract
Background: Patient recall of information about procedures, including risks and benefits and potential outcomes, is often insufficient. We sought to determine whether a multimedia educational tool enhances the informed consent discussion for elective neurosurgical procedures by increasing patient knowledge of the procedure. Methods: Adult patients from a single neurosurgical site eligible for 4 neurosurgical procedures (lumbar spine or cervical spine decompression for degenerative disease, craniotomy for brain tumour or trigeminal neuralgia treatment) were offered enrolment. Patients were randomly assigned to either the control arm (standard consent discussion) or the intervention arm (review of an e-book containing information tailored to their disease/injury plus standard consent discussion). Participants completed a 14-item questionnaire before and after the consent discussion. Results: Questionnaires were completed by 38 participants, 18 in the control group and 20 in the intervention group. The mean age was 62.2 (standard deviation [SD] 13.6) years and did not differ significantly between the 2 groups. The mean baseline questionnaire scores were similar for the control and intervention groups (20.4 [SD 7.3] v. 20.6 [SD 6.7]). However, the mean scores on the follow-up questionnaire were significantly different between the 2 groups (20.2 [SD 4.0] v. 23.2 [SD 4.9], p = 0.02). There was no change in the scores on the 2 questionnaires in the control group, whereas, in the intervention group, the mean score was significantly higher after the intervention (p = 0.03). Conclusion: The use of an electronic booklet appears to improve patients’ knowledge of their surgical procedure. The use of multimedia booklets in clinical practice could help standardize and optimize the consent process, ensuring that patients receive the relevant information to make a truly informed decision.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".