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Record W4200542734 · doi:10.1111/resp.14149_144

O24‐5: Altered megakaryocyte and platelet parameters in idiopathic pulmonary fibrosis

2021· article· en· W4200542734 on OpenAlexaff
Shigeki Saito, Chung Cheng, Nebil Nuradin, Joseph A. Lasky, Wenying Lu, Mathew Suji Eapen, Tillie‐Louise Hackett, Gurpreet K. Singhera, James Markos, Greg Haug, Collin Chia, Josie Larby, Samuel James Brake, Glen Westall, Jade Jaffar, Rama Satyanarayana, R.S.R. Kalidhindi, Nimesha De Fonseka, Venkatachalem Sathish, Singh Sohal

Bibliographic record

VenueRespirology · 2021
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
FundersRoyal Australasian College of Physicians
KeywordsMedicineMegakaryocytePulmonary fibrosisPlateletCardiologyInternal medicineIdiopathic pulmonary fibrosisFibrosisLung

Abstract

fetched live from OpenAlex

were used to determine the prognostic significance of medication burden on transplant-free survival. Results: During the median follow-up of 2.5 years, 43% of IPF patients experienced adverse reactions leading to intolerance of antifibrotic medications within 6 months of initiation, with high baseline medication burden being an independent predictor of intolerance (medication count: p=0.02; polypharmacy: p=0.02; MRCI: p=0.01). This association was not observed for immunosuppressive medications in non-IPF ILD patients who had a lower rate of intolerability (18%). The MRCI was the only measure of medication burden that was associated with transplant-free survival in both cohorts (p<0.001 for both; Figure ), which improved prognostication beyond known clinical factors and the ILD-GAP index. Conclusions: Medication burden affects tolerance for antifibrotic medications in patients with IPF. Medication regimen complexity is superior to simple evaluation of medication burden for predicting prognosis in ILD.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.865

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 integrity0.0000.000
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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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
Published2021
Admission routes1
Has abstractyes

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