Bibliographic record
Abstract
Collaborative chronic care networks of the future will be major contributors to advances in clinical knowledge. What will this future look like? Pediatric gastroenterologists at a majority of centers commit to working together to improve the care and outcomes of their patients, forming a network in which data from all patient visits are collected for a common database; analyses are used immediately for quality improvement and subsequently for research; open-source science ensures that patient data are widely available for investigation; and a federated (centralized) institutional review board provides efficient and effective protection of patient confidentiality. The clinician quickly enters information about the patient during each clinic visit on a standardized form in the electronic health record; the data are electronically extracted and made accessible to the common database, directly or through a local node; data-in-once ensures that patient data are not wasted but are used for clinical care, improvement, and research. The network provides frequent reports that clinicians use for clinical decisions, population management, previsit planning, self-auditing, and redesign of care delivery. Patients, families, clinicians, researchers, and improvement scientists communicate and work together in this collaborative chronic care network (C3N). Patient-reported outcomes are included as well. Is this realistic? The ImproveCareNow Network (1–5), which has grown to 36 centers with 300 pediatric gastroenterologists and 10,000 patients with Crohn disease and ulcerative colitis, is becoming such a C3N, a learning health system for improvement, innovation, and discovery. A Canadian network has an opportunity to do so as well (6).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.035 | 0.039 |
| Insufficient payload (model declined to judge) | 0.112 | 0.055 |
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".