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Record W3167683881 · doi:10.1186/s13223-021-00560-3

HealthSWEDE: costs with sublingual immunotherapy—a Swedish questionnaire study

2021· article· en· W3167683881 on OpenAlexvenueno aff
Petter Olsson, Carl Skröder, Lars Ahlbeck, Frida Hjalte, Karl-Olof Welin, Ulla Westin, Morgan Andersson, Cecilia Ahlström‐Emanuelsson, Lars‐Olaf Cardell

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2021
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
FundersKarolinska Institutet
KeywordsSlitSublingual immunotherapyMedicinePresenteeismAbsenteeismAllergyPopulationInternal medicineIndirect costsImmunotherapyPediatricsPhysical therapyImmunologyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this cross-sectional survey was to compare the health-economic consequences for allergic rhinitis (AR) patients treated with sublingual Immunotherapy (SLIT) in terms of direct and indirect costs with a reference population of patients receiving standard of care pharmacological therapy. METHODS: Primary objective was to analyse the health-economic consequences of SLIT for grass pollen allergy in Sweden vs reference group waiting for subcutaneous immunotherapy (SCIT). A questionnaire was mailed to two groups of AR patients. RESULTS: The questionnaire was distributed to 548 patients, 307 with SLIT and 241 in reference group (waiting for SCIT). Response rate was 53.8%. Mean annual costs were higher for reference patients than SLIT group; € 3907 (SD 4268) vs € 2084 (SD 1623) p < 0.001. Mean annual direct cost was higher for SLIT-patients, € 1191 (SD 465) than for reference, € 751 (SD 589) p < 0.001. Mean annual indirect costs for combined absenteeism and presenteeism were lower for patients treated with SLIT, € 912 (SD 1530), than for reference, € 3346 (SD 4120) p < 0.001, with presenteeism as main driver. CONCLUSIONS: SLIT seems to be a cost-beneficial way to treat seasonal AR. This information might be used to guide future recommendations.

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)0.000
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 score0.473
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.019
GPT teacher head0.332
Teacher spread0.312 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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