Validation of the St. Paul’s Endoscopy Comfort Scale (SPECS) for Colonoscopy
Bibliographic record
Abstract
AIMS: Patient comfort during colonoscopy is an important measure of quality, which can improve patient satisfaction and compliance with future procedures. Our aim was to develop and validate a pain assessment tool based on objective behavioural cues tailored to outpatients undergoing colonoscopy: St. Paul's endoscopy comfort score (SPECS). METHODS: A single-centre, prospective study was conducted in consecutive adults undergoing planned outpatient colonoscopy. Patient comfort was independently assessed by the physician, nurse and a research assistant (observer) using the SPECS and the Gloucester scale (GS). In addition, the nurse-assessed patient comfort score (NAPCOMS), nonverbal pain Assessment tool (NPAT) and Richmond agitation sedation scale (RASS) were completed by the observer. Data on subject demographics, sedation dose and duration of the procedure were collected. Following the procedure, patients completed a patient satisfaction questionnaire, including a visual analogue scale (VAS) to measure their overall perceived pain during the procedure. RESULTS: The study enrolled 350 subjects. The SPECS showed excellent inter-rater reliability among all three raters with an intra-class coefficient (ICC) of 0.81 (95% CI, 0.78-0.84), while the GS showed good reliability with an ICC of 0.77 (95% CI, 0.73-0.80). The SPECS demonstrated moderate agreement with the patient-reported VAS ratings. CONCLUSIONS: The St. Paul's endoscopy comfort score was successfully validated, demonstrating excellent inter-rater reliability.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".