The promise of rapid detection of active pulmonary tuberculosis in rural Alaska.
Bibliographic record
Abstract
BACKGROUND: The process by which active pulmonary tuberculosis (TB) is detected can be tediously slow in rural and often roadless Alaska, where several hundred air or boat miles can separate a patient from a chest x-ray and/or sputum collection. Additionally, the only TB reference lab in the state is many hundreds of air miles away, albeit centrally located in Anchorage. Under such conditions, it may take up to a week to process serial sputum AFB smears. This can result in either delayed onset of treatment or unnecessary empiric treatment, all while safety for the community is being considered. This dilemma often results in precautionary hospital isolation of a patient who might otherwise have been able to travel home by air. This article proposes a roadmap for remote health care settings that might bridge our current TB diagnostic ability to a better way in the future. METHODS: Current TB diagnostic guidelines in our area (Yukon-Kuskokwim Delta) were reviewed for integration of the Xpert MTB/ RIF assay with the purpose of improving TB health care while emphasizing patient benefits and cost savings. RESULTS: A clinical guideline that integrates the rapid TB assay into the current TB diagnostic algorithms for adults and adolescents is proposed. Crude cost savings at our hospital resulting from this guideline are estimated to be $316,000 per year. CONCLUSION: The proven utility of a new rapid TB diagnostic, the Xpert MTB/RIF assay, offers the promise of more efficient TB medical care, improved patient human rights and improved hospital and community environmental safety, all with likely huge reduced health care costs in remote Alaska.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".