Проблема информатизации лечебно-профилактических учреждений РФ (на примере ЛПУ г. Москвы)
Bibliographic record
Abstract
A study of the informatization level 56 health care facilities (HCF) in Moscow. Found that 100% of equipment information systems (IS) of the basic processes found only in 10,71% of the surveyed health facilities, about a third of health facilities (32,15%) has the power equipment from 50 to 88%, about 20% of health facilities equipped to 26-50%, a quarter of HCF (25%) is equipped with only 10-25%, a very low level of informatization (up 10%) 12,5% met in hospitals. The most frequently used in the design of IP applications for medicines, as well as registration services (59,18±5,64% and 51,20±6,1% respectively). Least helpful computerized process of registration of transactions (card transactions) 31,44±5,36%. In assessing the extent to which the functions of IP was found that 36,7% of health facilities in Moscow today use IP not completely, that is not efficient enough. Given the urgency of the application of information technology (IT) in the modern health care, its resource capabilities, the question of national informatization of health facilities in need of further development
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".