The impact of physician training and experience on the survival of patients with active tuberculosis
Bibliographic record
Abstract
BACKGROUND: Physician training and experience may be important factors influencing treatment outcomes of patients with tuberculosis. We conducted an analysis to evaluate physician and patient characteristics and their association with the rate of death among tuberculosis patients. METHODS: We retrospectively reviewed all reported cases of active tuberculosis in Toronto between July 1, 1999, and June 30, 2002. We obtained extensive clinical data on cases as well as information on the training and clinical experience of treating physicians. We subsequently identified factors associated with patient mortality in a survival analysis. RESULTS: In a multivariable Cox regression analysis involving 1154 patients, factors associated with all-cause mortality included patient age (in years) (hazard ratio [HR] 1.05, 95% confidence interval [CI] 1.04-1.07, p < 0.001), use of directly observed therapy (HR 0.22, CI 0.13-0.39, p < 0.001), receipt of care from a physician experienced with tuberculosis (per case managed per year) (HR 0.98, CI 0.97-0.99; p = 0.01) and admission to hospital during the course of treatment (HR 15.44, CI 7.06-33.76, p < 0.001). Factors that were not associated with patient survival included whether the physician graduated from a foreign medical school, the physician's medical specialty and the number of years in clinical practice. INTERPRETATION: Physician experience with tuberculosis and use of directly observed therapy positively influenced the survival of patients with active tuberculosis in our setting.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".