Is Textbook Outcome a valuable composite measure for short-term outcomes of gastrointestinal treatments in the Netherlands using hospital information system data? <i>A retrospective cohort study</i>
Bibliographic record
Abstract
OBJECTIVE: To develop a feasible model for monitoring short-term outcome of clinical care trajectories for hospitals in the Netherlands using data obtained from hospital information systems for identifying hospital variation. STUDY DESIGN: Retrospective analysis of collected data from hospital information systems combined with clinical indicator definitions to define and compare short-term outcomes for three gastrointestinal pathways using the concept of Textbook Outcome. SETTING: 62 Dutch hospitals. PARTICIPANTS: 45 848 unique gastrointestinal patients discharged in 2015. MAIN OUTCOME MEASURE: A broad range of clinical outcomes including length of stay, reintervention, readmission and doctor-patient counselling. RESULTS: Patients undergoing endoscopic retrograde cholangiopancreatography (ERCP) for gallstone disease (n=4369), colonoscopy for inflammatory bowel disease (IBD; n=19 330) and colonoscopy for colorectal cancer screening (n=22 149) were submitted to five suitable clinical indicators per treatment. The percentage of all patients who met all five criteria was 54%±9% (SD) for ERCP treatment. For IBD this was 47%±7% of the patients, and for colon cancer screening this number was 85%±14%. CONCLUSION: This study shows that reusing data obtained from hospital information systems combined with clinical indicator definitions can be used to express short-term outcomes using the concept of Textbook Outcome without any excess registration. This information can provide meaningful insight into the clinical care trajectory on the level of individual patient care. Furthermore, this concept can be applied to many clinical trajectories within gastroenterology and beyond for monitoring and improving the clinical pathway and outcome for patients.
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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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".