Nothing will ever be as before. Reflections on the COVID-19 epidemics by nephrologists in eleven countries
Bibliographic record
Abstract
It is difficult to define the COVID-19 epidemics: it is the black swan challenging what we thought we knew, confronting countries with a high level of technology with problems that seemed to belong to the past.It is a disaster foretold, predicted by prophetic writers and enlightened politicians; it is the fraudulent mistake that has put everyone in the world at risk… However we see it, this epidemic is also a lifechanging experience for patients and physicians [1][2][3].For this reason, we decided to ask to some young-but not-too-young colleagues who currently work in clinical practice in 11 different countries to tell us something about their experience with the COVID-19 epidemic.They were not selected on the basis of a brilliant curriculum, or a list of outstanding publications, but simply invited as friends, or friends of friends.Most of them answered.The questions were straightforward, touching rapidly on the logistics involved, and also regarding the fears and the hopes engendered by being confronted with "the infection".The answers, summarized and commented on in this editorial, should make us reflect not only on the impact of the epidemics, but also, in a broader sense, on the way the "next generation" of our colleagues is reacting and how they will probably integrate the lessons learnt now in the long years of their future clinical practice.The first question was simple: please, introduce yourself and your work.Yet, albeit simple, the answers, which mirror our different cultures, are interesting: many did not write their names, and two completely skipped the presentation, as if their names mattered little in comparison to the problem they were going to discuss.I'm 30 years old.I'm a nephrologist working in Italy, in the city of Bari (Puglia).I work in the COVID unit in the Policlinico, a large university hospital.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".