Changes of the system of postoperative care decreases mortality in a surgical unit
Bibliographic record
Abstract
In 2005, 10 public health care institutions functioned in the area of Lodz, having in their structure a surgical unit classified as a general surgery unit. They were three university teaching hospitals, three provincial hospitals, three county hospitals and one departmental hospital. Mortality in university teaching hospitals having 167 beds was 1.25%, in provincial hospitals with 191 beds was 2.96%, and in county hospitals with 140 beds was 3.98%. The lowest percentage mortality was noted in the surgical unit of Bolesùaw Szarecki University Teaching Hospital No. 5 in Lodz (UH No. 5) and it was 0.35%. The authors decided to analyse the causes of such low mortality in this hospital. Two remaining university teaching hospitals, N. Barlicki University Teaching Hospital No. 1 in Lodz (UH No. 1) and WAM University Teaching Hospital No. 2 in Lodz (UH No. 2), were selected for comparative analysis. The selection was dictated by a few reasons. The Medical University in Lodz is the founding body of all hospitals subjected to analysis. These hospitals are only a few kilometres away from each other. The units have a similar number of beds, and well-educated medical and nursing staff. Heads of the hospital departments have all been awarded a professorship. Health benefits are provided on the basis of the same list of benefits as part of contract with the same payer – Lodz Provincial Branch of the National Health Fund.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".