TSN® Database Australia, a new tool to monitor antimicrobial resistance in Australia
Bibliographic record
Abstract
An electronic network of Australian microbiology laboratories was established to monitor the emergence and occurrence of antimicrobial resistance among clinically relevant bacteria. It is believed that the data network collected approximately 42 per cent of all antibacterial susceptibility test results generated by Australian laboratories. The network comprised 94 hospitals and 9 private commercial laboratories. Selected data elements were extracted and electronically transmitted to a central location. Upon receipt, all data were first normalised and thereafter examined for errors. Duplicate results for the same patient were identified to prevent skewing of the data toward resistance. All data passing quality assessment was staged for release of a new database release that occurred monthly. Unusual test results were first validated prior to their inclusion into the database. Using an Internet-based query tool, individual institutions could query their own data, but could only query aggregated data for other regional or national analyses. Individual patient results could be examined nor could the results of any individual institution other than their own. As of March 2002, TSN Database Australia contained 14,648,752 test results, from 2,000,394 strains (453 different taxa) and 1,213,605 patients. Since the same database concept has been established in 10 other countries (United States of America, Europe, and Canada), observations made in Australia may be compared to those observed elsewhere in the world. This article will describe TSN in greater detail, describe the query tool and some of the analyses that are possible. Commun Dis Intell 2003;27 Suppl:S67-S69.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".