Computer-assisted instruction before colonoscopy is as effective as nurse counselling, a clinical pilot trial
Bibliographic record
Abstract
BACKGROUND AND STUDY AIMS: Better patient education prior to colonoscopy improves adherence to instructions for bowel preparation and leads to cleaner colons. We reasoned that computer assisted instruction (CAI) using video and 3 D animations followed by nurse contact maximizes the effectiveness of nurse counselling, increases proportion of clean colons and improves patient experience. PATIENTS AND METHODS: Adults referred for colonoscopy in a high-volume endoscopy unit in the Netherlands were included. Exclusion criteria were illiteracy in Dutch and audiovisual handicaps. Patients were prospectively divided into 2 groups, 1 group received nurse counselling and 1 group received CAI and a nurse contact before colonoscopy. The main outcome, cleanliness of the colon during examination, was measured with Ottawa Bowel Preparation Scale (OBPS) and Boston Bowel Preparation Scale (BBPS). We assessed patient comfort and anxiety at 3 different time points. RESULTS: We included 385 patients: 197 received traditional nurse counselling and 188 received CAI. Overall patient response rates were 99 %, 76.4 % and 69.9 % respectively. Endoscopists scored cleanliness in 60.8 %. Comparative analysis of the 39.2 % of patients with missing scores showed no significant difference on age, gender or educational level. Baseline characteristics were evenly distributed over the groups. Bowel cleanliness was satisfactory and did not differ amongst groups: nurse vs. CAI group scores in BBPS: (6.54 ± 1.69 vs. 6.42 ± 1.62); OBPS: (6.07 ± 2.53 vs. 5.80 ± 2.90). Patient comfort scores were significantly higher (4.29 ± 0.62 vs. 4.42 ± 0.68) in the CAI group shortly before colonoscopy. Anxiety and knowledge scores were similar. CONCLUSION: CAI is a safe and practical tool to instruct patients before colonoscopy. We recommend the combination of CAI with a short nurse contact for daily practice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".