Flora Analysis of Clinical Infectious Specimen
Bibliographic record
Abstract
OBJECTIVE To study the distribution of clinical infectious bacterium spectrum,to reveal infections characteristic and epidemiology,and to provide basis for clinical diagnosis and therapy.METHODS Isolating,culturing and identifying 1201 clinical infectious specimens, to survey multiple drug resistance(MDR) in main isolated bacteria.RESULTS All of isolated bacteria were 1275 strains.Mixed infectious rate of the specimens was 6.2%. Gram negative bacilli were major(56.5%).Main florae of the infectious bacterium spectrum were Staphyococcus(16.0%),Saccharomyces(15.8%),Pseudomonas(13.7%),Escherichia(13.2%) and Klebsiella(11.7%).The main composition of Staphylococcus was S.haemolyticus,S.aureus, S.epidermidis. Their resistant rate to meticillin was 72.4%,16.3%,66.1%,respectively.The vancomycin resistant Enterococcus was 5.2%,E.coli was 23.2%, K.pneumoniae was 28.2%,Enterobacter cloacae of producing extended spectrum β lactamase strains(ESBLs) was 10.5%.CONCLUSIONS To normal flora and to opportunistic pathogens were main strains in the infectious baterium spectrum, hospital would face with the problem of more MDR and mixed infection. We must improve means of treatment on clinical work and use antibiotic rationally to reduce production and transmission of MDR.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".