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Record W3083599299 · doi:10.1093/pubmed/fdaa146

Factors influencing treatment outcomes of tuberculosis patients attending health facilities in Galkayo Puntland, Somalia

2020· article· en· W3083599299 on OpenAlexaff
Said Abasse Kassim, André Côté, Said Mohamed Kassim, Muhammad Abbas, Mirza Muhammad Faran Ashraf Baig, Abdiweli Mohamed Ahmed, Mahamoud M Hussein, Xiaobo Li, Rui Chen

Bibliographic record

VenueJournal of Public Health · 2020
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMedicineTuberculosisLogistic regressionOdds ratioMultivariate analysisPublic healthTb treatmentCross-sectional studyOddsFamily medicinePediatricsInternal medicineNursing

Abstract

fetched live from OpenAlex

AIM: This study evaluated the underlying factors associated with poor tuberculosis (TB) treatment outcomes among patients attending health care facilities in Galkayo, Puntland, Somalia. METHODS: An institution-based cross-sectional study was conducted between 2016 and 2017 in three selected TB clinics. Data were collected from 400 TB patients, through medical record review and structured questionnaire. Multivariate logistic regression analyses were performed. RESULTS: Of the 400 TB respondents, 57.3% were new cases, 12.3% had smear-negative TB and 12.5% had extrapulmonary TB. The median age was (35.66 ± 13.16) with majority being male (65.5%). Overall, 85% of patients were successfully treated, 9.7% failed and 5.3% defaulted. Multivariate analysis revealed that patient's body weight (odds ratio [OR]: 1.078); diabetes (OR: 8.022); family size (OR: 3.851); patients' delay in diagnosis (OR: 11.946); frequency of receiving anti-TB medication (OR: 9.068); smoker (OR: 5.723); category of patients (retreatment versus new, OR: 5.504; retreatment versus transfer in, OR: 4.957); health facilities (OR: 6.716) and treatment duration (OR: 132.091) were independent factors associated with poor TB outcomes. CONCLUSIONS: Our findings highlight the need to improve TB services for vulnerable groups. They also emphasize the need for health system strengthening, public awareness and risk of treatment interruption. This may reduce both patients' delay in seeking care and TB treatment failure in Galkayo district.

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)0.003
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 score0.112
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
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 head0.176
GPT teacher head0.395
Teacher spread0.219 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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