ECMO: Improving our results by chasing the rabbits
Bibliographic record
Abstract
As Marcelo Giugale published in the Financial Times, Latin America, on the whole, has not excelled at innovation - doing the same things in a new and better way or at doing new things. It has been slow to acquire, adopt and adapt technologies by this time available in other places[1]. Although extracorporeal membrane oxygenation (ECMO) is not a new technology, its use in Latin America is not widespread as needed. Furthermore, we still have a number centers doing ECMO, not reporting their cases, lacking a structured training program and not registered with the extracorporeal life support organization (ELSO). With this scenario, and accepting that ECMO is the first step in any circulatory support program, it is difficult to anticipate the incorporation of new and more complex devices as the technologically advanced world is currently doing. However, the good news is that with the support of experts from USA, Europe and Canada the results in Latin America ELSO'S centers are improving by following its guidelines for training, and using a standard educational process. There is no doubt that we can learn a great deal from the high velocity organizations - the rabbits - whom everyone chases but never catches, that manage to stay ahead because of their endurance, responsiveness, and their velocity in self-correction[2].
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".