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Record W2417796199

Extent of pulmonary tuberculosis in patients diagnosed by active compared to passive case finding.

2004· article· en· W2417796199 on OpenAlexaffabout
Heather Ward, Darcy D. Marciniuk, Punam Pahwa, Vernon Hoeppner

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of SaskatchewanRoyal University Hospital
Fundersnot available
KeywordsMedicineGynecologyMedical screeningRespiratory diseasePulmonary tuberculosisTuberculosisInternal medicinePathologyLung
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the extent of pulmonary tuberculosis amongst patients detected by screening (active case finding) with that in patients detected by symptoms (passive case finding), and to identify early symptoms of pulmonary tuberculosis. SUBJECTS AND METHOD: In this cross-sectional study, Tuberculosis Control Program records were reviewed for method of detection and extent of disease in Canadian Plains Aborigines between 1 January 1991 and 30 June 1999. RESULTS: Among 903 cases, method of detection was active in 450 (49.8%) and passive in 453 (50.2%). Cough and fever were the most common symptoms in both methods of detection, and were significantly more frequent in passive detection (P < 0.05). Cough was present in 59% and fever in 19% of actively detected cases compared to 84% and 47%, respectively, of passively detected cases. Age was significantly different between the two methods of detection. Hemoptysis, weight loss and method of detection were associated with increased risk of infectiousness among those < or = 19 years, while cough, hemoptysis and weight loss were associated among those >19 years. CONCLUSION: Method of detection rather than age contributed to infectiousness in children and adolescents. Daily cough for more than 1 month and unexplained fever for more than 1 week should raise the suspicion for TB.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.292
Teacher spread0.264 · 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

Citations59
Published2004
Admission routes2
Has abstractyes

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