мАТЕмАТИЧЕСКОЕ мОДЕЛИРОвАНИЕ СОСТОЯНИЯ ЗДОРОвьЯ мЕДИЦИНСКИХ РАБОТНИКОв
Bibliographic record
Abstract
Analg. — 2002. — Vol. 95. — P. 1726–1730.9. Flabouris A., Seppelt I. Optimal interhospital tran-sport system for the criticaly III. Yearbook of intensive care and emergency medicine ed. by J.-L. Vincent Springer. — Verlag: Berlin, 2001. — P. 647–661.10. Gemke R., Bonsel G., van Vught A. Effectiveness and efficiency of a dutch pediatric intensive care unit // Crit. Care Med. — 1994. — Vol. 22. — P. 1477–1484.11. Graf J., Graf C., Janssens U. Analysis of resource use and cost generating factors in a German medical intensive care unit // Intens. Care Med. — 2002. — Vol. 28. — P. 324–331.12. Keenan S., Dodek P. Intensive care unit admission has impact on long-term mortality // Chan. Crit. Care Med. — 2002. — Vol. 30. — P. 501–507.13. McLean R., Tarshis J., Mazer D. Death in two Canadian intensive care units // Crit. Care Med. — 2000. — Vol. 28. — P. 100–103.14. Ridley S., Chrispin P., Scotton H. Changes in quality of life after intensive care // Anaesthesia. — 1997. — Vol. 52. — P. 195–202.15. Stenhouse C.W., Bion J.F. Outreach: a hospital-wide approach to critical illness. In: Yearbook of intensive care and emergency medicine 2001, ed. by J.-L. — Vincent Springer-Verlag. Berlin, 2001. — P. 661–675.16. Understanding costs and cost-effectiveness in critical care. Report from the second American thoracic society. Workshop on outcome research // Am. J. Respir. Crit. Care Med. — 2002. — Vol. 165. — P. 4.17. Wilson R.M. The quality in Australian health care study // Med. J. Aust. — 1995. — Vol. 163. — P. 458–471.18. Zbinden A. Introducing a balanced scorecard mana-gement system in a University anesthesiology department // Anesth. Analg. — 2002. — Vol. 95. — P. 1731–1738.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.045 |
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; both teacher heads agree on what is shown here.
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".