Bibliographic record
Abstract
Medical documentation poses many challenges in acute emergencies. Time and again, the reflection of those who manage healthcare during a ‘disaster’ involves some reference to poor, inadequate or even absent documentation. The reasons for this are manifold, some of which, it is often argued, would be negated by using technological solutions. Smartphones. Tablets. Laptops. Networks. Many models exist, and yet we have not reached a status quo whereby this single aspect of disaster response is fixed. Should we abandon technology in favour of a traditional paper solution? Perhaps not; however, it seems that the answer may lie somewhere in between. As simple as the problem might seem on the surface, its answer requires thought, investment and practice. And while it is being answered, it is essential to remain mindful of the hazards posed by gathering healthcare data: who owns it? Where will it be stored? How will it be shared? Academics and practitioners are equal guests at the table wherein this challenge is approached.
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 | 0.029 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".