Централизация лабораторных исследований как один из методов совершенствования лабораторной службы
Bibliographic record
Abstract
This paper focuses on efficacy of centralization of hospital laboratory units in the town of Oktyabrsky of Bashkortostan Republic. Hospital laboratory equipment deterioration was 90,0%. Centralization contributed to the organization of a centralized clinicodiagnostic and a bacteriologic laboratory. In hospitals, 5 clinical laboratories for performing routine surgeries and 3 express laboratories were available. Staff reduction by 8,3% allowed to increase staffing levels by 14,8%. Laboratory centralization has contributed to economic benefits. In 2012, laboratory service costs in the town o9f Oktyabrsky made up 59 769 632,81 roubles and after 2014 centralization – 58 415 170,77 roubles meaning a 2,3% reduction. The cost structure for laboratory service activities has changed. In 2012, operating costs made up 11%, disposable materials – 39%, labour payment – 50%. In the first quarter of 2014, operating costs made up 4,1%, disposable materials – 54,8%, labour payment – 41,1%.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".