Bibliographic record
Abstract
Objective To understand the basic situation of nationwide TB special hospitals,and their work in TB case-finding,registration,reporting,referral in order to further provide evidence base for cooperation between tuberculosis special hospitals and TB control institutions. Methods Investigation forms were designed by NCTB and distributed to all eligible TB special hospitals through provincial CDC. NCTB made a summary and analysis of this investigation. Results There are a total of 77 eligible TB special hospitals nationwide,among which hold 210 000 professional and technical personnel,with predominance of middle and high level staff in terms of professional title (52.%); In 1st Quarter 2007,there are a total of 269 000 person-times of outpatients service,32 000 TB cases were reported to internet-based PTB reporting system,accounting for 9.6% of 335 000 which is total cases reported in this system during same period. 26 000 TB cases were referred to TB institutions,accounting for 9.7% of outpatient service of TB institutions. Conclusions TB specialist hospitals have a certain number of technical personnel and capacity in patient treatment,did a large amount of work for TB control in China.Therefore,there is a need to incorporate TB special hospitals into TB prevention and control system as soon as possible,and strengthen the cooperation between TB control institutions and TB special hospitals,establish an effective mechanism for long-term cooperation. This will be one of important component for future TB control work in China.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".