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Record W2521946223 · doi:10.5588/pha.16.0007

Did FIDELIS projects contribute to the detection of new smear-positive pulmonary tuberculosis cases in China?

2016· article· en· W2521946223 on OpenAlexfundno aff
Yu Kuei Lin, C-Y. Chiang, I. D. Rusen, Sven Gudmund Hinderaker, Ana Roldan, Einar Heldal, D. A. Enarson, L-X. Zhang

Bibliographic record

VenuePublic Health Action · 2016
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsMedicinePulmonary tuberculosisChinaPopulationIntervention (counseling)Significant differenceTuberculosisDemographyPediatricsInternal medicineEnvironmental healthPathologyGeographyNursing

Abstract

fetched live from OpenAlex

Setting: The first phase of the Fund for Innovative DOTS Expansion through Local Initiatives to Stop TB (FIDELIS) projects in China started in 2003. Objective: To determine whether the FIDELIS projects contributed to the increased case detection rate for new smear-positive pulmonary tuberculosis (PTB) in China. Methods: We compared the case notification rates (CNRs) in the intervention year with those of the previous year in the FIDELIS areas, then compared the difference between the CNRs of the intervention year and the previous year in the FIDELIS areas with those in the non-FI-DELIS areas within the province. Results: There was an increase in the CNR in the intervention year compared with the previous year for all the project sites. The differences between the CNR in the intervention year and the previous year ranged from 6.4 to 31.1 per 100 000 population in the FIDELIS areas and from 2.9 to 20.4/100 000 in the non-FIDELIS areas. Differences-in-differences analysis shows that the differences in the CNRs in the FIDELIS areas were not statistically significantly different from those in the non-FIDELIS areas ( P = 0.393). Conclusion: The FIDELIS projects may have contributed to the increase in case detection of new smear-positive PTB in China, but the level of evidence is low.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.384
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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