Disseminating Knowledge in Intestinal Failure: Initial Report of the Learn Intestinal Failure Tele‐ECHO (LIFT‐ECHO) Project
Bibliographic record
Abstract
BACKGROUND: Intestinal failure (IF) is defined as an ultrarare disease, with an estimated prevalence of ∼25,000 cases in the US. There is a suspicion of disparities in outcomes in IF care, likely related to widespread lack of expertise. The Extension for Community Healthcare Outcomes (ECHO) model originally described by Dr Sanjeev Arora has been used to disseminate knowledge and best practices in many chronic diseases to improve outcomes. We examined our initial experience with using the ECHO model to disseminate learning in IF. METHOD: This is a retrospective review of the launch, growth, and geographic reach of the Learn Intestinal Failure TeleECHO (LIFT-ECHO) program using prospectively collected data. RESULTS: The LIFT-ECHO program has achieved significant geographic reach and clinician engagement. The program has reached close to two-thirds of the states in the US and several countries outside. Clinician engagement in the learning program appears to be growing exponentially. CONCLUSION: It is feasible to use the ECHO model to disseminate knowledge in managing a rare disease like IF while maintaining fidelity to the proven model. Studies are underway to demonstrate direct benefit to 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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".