Impact of referral system on the registration rate of smear positive pulmonary tuberculosis patients in China
Bibliographic record
Abstract
Objective To assess the impact of the referral system on the registration rate of smear positive (S+) pulmonary tuberculosis (PTB) patients. Methods The data of registered S+ PTB patients in the NTP Quarterly Report from the first quarter in 2002 to the third quarter in 2004 in 31 provinces / municipalities / autonomous regions in China were analyzed so as to understand the constituent ratio of S+ PTB in the first visit patients from different sources. A regression analysis was made to observe the correlation between the total registration rate of S+ PTB and the registration rate of referred S+ PTB. Results According to the analysis of the category of the registered S+ PTB patients in China, the PTB patients from consulting for symptoms ranked the first which accounts for 57.5% of the total S+ PTB patients; referred PTB patients was the second which occupies 32.9%; the third category was the focus and regular recommendation which accounted for 9.6% of the total. The proportion of referred S+ PTB patients in the total S+ PTB patients was 35.2% in 2004, higher than it was in 2002 and 2004. Among the 31 provinces / municipalities / autonomous regions in China, the proportion of referred S+ PTB patients in the total S+ PTB patients was over 50% in 5 provinces; over 40% in 2 provinces; over 30% in 8 provinces; over 20% in 13 provinces, and lower than 20% in 3 provinces. The analysis of the correlation between the total registration rate of S+ PTB and the registration rate of referred S+ PTB showed the association was statistically significant (F=86.31, P0.01). The correction formula was Y=2.0917X+8.455 excluding the outlier. The significant test of the coefficient of correlation showed it was statistically significant (F=131.46, P0.01). Conclusion To strengthen the referral system is a key method to increase the detection of S+ PTB patients in China. In half of the 31 provinces / municipalities / autonomous regions, referral has become the main source of S+ PTB patients. Referral rate and registration rate of S+ PTB patients are positively correlated.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".