It was Lauren - and she just wanted to help. Putting the human side back into the critical illness experience
Bibliographic record
Abstract
There’s something diabolical about Critical Illness: the illness itself is so severe, that it strips its victim of all control. Regardless of the road to it - whether by slow illness with loss of function followed by sharp decline, or by sudden brutal Trauma, the inability to move, speak, think straight, and have dignity from clothing, has to be the most terrifying and dehumanizing of experiences.But the diabolical nature of critical illness is not found there. It is found in the fact that it mandates a rapidity and Intensity of Care that by its very definition compounds the dehumanization. Even when done “nicely”, insertion of a chest tube, central line or urinary catheter, endotracheal suctioning, or any of the litany of other things we need to do are painful at the least, and injurious to the soul at the worst. It is found in that fact that Critical Illness forces very caring people to do things that injure and leave scars- when that is the farthest thing from what they wish for their charges. Finally, it is found in the very nature of the Critical Care Environment which isolates patients’ families from them, at a time when exactly the opposite may be needed. So how do we put the human connection back into Critical Care? Impossible? A huge task? Actually, it’s a very small one; it’s the Little Things that Matter…
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".