Bibliographic record
Abstract
OBJECTIVE To investigate the antibiotic resistance of Proteus, evaluate the antimicrobial susceptibility testing methods, and give some suggestions for clinical antibiotic usage.METHODS Forty five clinical isolates were collected from Aug 1998 to Dec 1999 in General Hospital and the 304th hospital of PLA. They were all tested by disk diffusion method, double disk synergy test(DDST), Etest and confirmatory test of disk diffusion.RESULTS (1) All the isolates displayed high sensitivity to imipenem(IMP). The sensitivity to sulfamethoxazole co(SMZ co ), ciprofloxacin(CIP) and gentamicin(GEN) was low. (2) The testing results of three methods of DDST, Etest and confirmatory test of disk diffusion showed well consistent. (3) Cefotaxime(CTX) was a more sensitive indicator than ceftazidime(CAZ) in screening ESBLs producing Proteus (P0.05). CONCLUSIONS (1) Imipenem can be used as the best drug for ESBLs producing Proteus infections. (2) The combination of disk diffusion method and double disk synergy test was a better method for its high sensitivity and specificity, low cost and easy to implement. (3) Cefotaxime was a more sensitive indicator than ceftazidime in screening ESBLs producing Proteus. It is suggested that using both drugs in screening ESBLs producing Proteus in Beijing city improve the detecting rate.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".