Effects of restructuring on the performance of microbiology laboratories in Alberta.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the error rates of organism identification and antibiotic susceptibility proficiency testing challenges before, during, and after microbiology laboratory restructuring in Alberta. METHODS: Alberta Health substantially reduced and redistributed laboratory funds to the regional health authorities in 1995, forcing a dramatic restructure of services. Many rural hospitals expanded their microbiology test menus, and urban centers consolidated microbiology testing into a centralized high-volume laboratory. The Laboratory Proficiency Testing Program of the College of Physicians and Surgeons of Alberta mailed regular test profile surveys to microbiology laboratories during the restructure period to determine the type and extent of changes in services. Based on the types of tests and the extent of analysis being done, most rural B-level and some C-level laboratories were reclassified to the A level. The Laboratory Proficiency Testing Program reviewed the error rates of proficiency challenges based on the performance of different levels of laboratories before and after the period of restructure. RESULTS: Overall performance has improved according to the number of errors documented on identification and susceptibility challenges for laboratories that remained at the same classification (ie, A or C). The number of major identification errors for laboratories that were reclassified increased, but the rate of major susceptibility errors decreased. More reclassified laboratories do not have dedicated registered technologist(s) who perform microbiology testing and are not supervised by an on-site pathologist and/or medical microbiologist compared with laboratories that remained at the same classification. CONCLUSIONS: Microbiology laboratory restructuring will have adverse effects on the quality of complex testing if experienced technologists are not retained and services are not medically supervised.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".