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Record W3170856576 · doi:10.1093/ecco-jcc/jjab076.764

P644 Hospitalization and abdominal surgery rates in CD according to drug-dispensing: a temporal trend analysis from the Brazilian public healthcare national system

2021· article· en· W3170856576 on OpenAlexaff
D O Magro, Paulo Gustavo Kotze, Abel Botelho Quaresma, Adérson Omar Mourão Cintra Damião, D A Valverde, Remo Panaccione, Stephanie Coward, Siew C. Ng, Gilaad G. Kaplan, C S R Coy

Bibliographic record

VenueJournal of Crohn s and Colitis · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineAbdominal surgeryPoisson regressionHealth carePublic healthInternal medicineConfidence intervalEmergency medicineSurgeryEnvironmental healthPopulationPathology

Abstract

fetched live from OpenAlex

Abstract Background The impact of current medical options in Crohn’s disease (CD) on hospitalization and surgical rates may be conflicting, and there is lack of data in newly industrialized countries. This study aims to describe temporal trends of proportional hospitalization and CD-related abdominal surgery rates according to drug-dispensing in Brazil, using public healthcare datasets. Methods All CD patients from the unique public healthcare national system (DATASUS) were included from January 2012 to December 2020 and identified according to ICD codes, medication or CD-related procedures. Data extraction was performed with the platform “TT Disease Explorer” (Techtrials Healthcare Data Science), which collects publicly available data via electronic algorithms with automatic updates. Annual rates of all-cause hospitalization and CD-related abdominal surgical procedures were captured and stratified by type of drug dispended. Average Annual Percent Change (AAPC) and 95% confidence intervals (CI) were calculated using poisson (or negative binomial) regression. Results The absolute number of registries of overall drug-dispensing for CD was 178,209, being 32.03% for Azathioprine (AZA), 10.91% for infliximab (IFX) and 10.52% for Adalimumab (ADA). AZA dispensing increased from 28.60% to 30.83% (AAPC 1.15; CI 0.23–2.09; p=0.015), ADA increased from 5.98% to 12.03% (AAPC 8.79; CI 6.33–11.30; p<0.001) and IFX increased from 7.09% to 12.03% (AAPC 7.52; CI 6.94–8.10; p<0.001) (figure 1). A total of 39,161 hospitalizations (all-cause) were captured in the same period. Hospitalization rates with AZA varied from 37.09% to 36.35% (AAPC -0.42, CI -1.08-0.24; p=0.209); for ADA remained stable (13.16% to 13.12%, AAPC -0.03; CI -1.10-1.05; p=0.962) and for IFX increased from 17.93% to 22.49% (AAPC 3.21; CI 1.66–4.79, p<0.001) (figure 2). Regarding CD-related abdominal surgical procedures (n=1181), rates were stable for AZA (AAPC 1.34; CI -8.41–12.12; p=0.797). Considering the use of anti-TNF agents, rates were stable with ADA, varying from 26,7% to 20,0% (AAPC -1.64; CI -13.84-12.29; p=0.807) and decreased from 33,3% to 4,5% for IFX (AAPC -17.05; CI -28.19- -4.17; p=0.011) (figure 3). Conclusion In this large national study, there was an increase in the number of dispensings of AZA, IFX and ADA for CD from 2012–2020 in the public healthcare system in Brazil, due in part to the increasing prevalence of CD. All-cause hospitalization rates remained stable for AZA and ADA, and increased in IFX patients. A reduction in CD-related abdominal surgical procedures was observed in patients who used IFX and were stable with AZA and ADA. These data can be used for future strategic planning in the national public healthcare system (SUS) in CD management in Brazil.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.103
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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".

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Citations2
Published2021
Admission routes1
Has abstractyes

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