Non‐tuberculous mycobacterial infections at <scp>S</scp>an <scp>F</scp>rancisco <scp>G</scp>eneral <scp>H</scp>ospital
Bibliographic record
Abstract
BACKGROUND AND AIMS: The epidemiology of non-tuberculous mycobacteria (NTM) infection is not well defined. We evaluated the trends in incidence of NTM infections at San Francisco General Hospital (SFGH), a large metropolitan county hospital. METHODS: We performed a retrospective review of microbiologic and clinical records of all patients with a positive NTM culture reported from 1993 to 2001. NTM infection was defined by the isolation of >1 NTM from any clinical specimen. Patients were stratified by human immunodeficiency virus (HIV) status. Univariate and multivariate logistic regression were used to identify factors that were independently associated with NTM infection. Trends over time were assessed using Poisson test for trend. RESULTS: During the study period, 25 736 samples from 7395 patients were cultured for mycobacteria. Of these samples, 2853 (11.1%) from 1345 patients (18.2%) were culture positive for NTM. Patient characteristics associated with infection included younger age (P < 0.001), male gender (P < 0.001), White ethnicity compared with Asian and Hispanic (P < 0.001 and P = 0.01, respectively), and HIV positivity (P < 0.001). Overall, NTM infection at SFGH decreased significantly from 319 cases in 1993 to 59 in 2001 (P < 0.001). Mycobacterium avium was predominant in both HIV-positive and HIV-negative populations (74.5% and 44.6% of isolates, respectively), and Mycobacterium kansasii was the second most common NTM species isolated. The proportion of other NTM species isolated in these groups differed. CONCLUSION: In contrast to other published studies, time-series analyses show that NTM isolation rates decreased during the study period at SFGH, where NTM was most strongly associated with HIV infection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".