Teleclinical Microbiology: An Innovative Approach to Providing Web-Enabled Diagnostic Laboratory Services in Syria
Bibliographic record
Abstract
OBJECTIVES: Telemedicine can compensate for the lack of health care specialists in response to protracted humanitarian crises. We sought to assess the usability of a teleclinical microbiology (TCM) program to provide diagnostic services in a hard-to-reach region of Syria. METHODS: A semimobile station was equipped with conventional micrograph and macrograph digital imaging systems. An electronic platform (Telemicrobiology in Humanitarian Crises, TmHC) was created to facilitate sharing, interpreting, and storing the results. A pilot study was conducted to identify the bacterial species and antimicrobial susceptibility pattern of 74 urinary clinical isolates. An experience survey was conducted to capture the feedback of 8 participants in the program. RESULTS: The TmHC platform (https://sdh.ngo/tmhc/) enabled systematic transmission of the laboratory records and co-interpretation of the results. The isolates were identified as Escherichia coli (n = 61), Klebsiella pneumoniae (n = 12), and Proteus mirabilis(n = 1). All the isolates were multidrug resistant. The performance of our TCM module was rated 4 (satisfying) and 5 (very satisfying) by 6 and 2 users, respectively. Data security of and cost-effectiveness were the main perceived concerns. CONCLUSIONS: Although we encountered several context-related obstacles, our TCM program managed to reach a highly vulnerable population of 4 million people confined in the northwest region of Syria.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".