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Record W3217498420 · doi:10.1136/gutjnl-2021-326544

Response by Abrahami et al to letter regarding article ‘Proton pump inhibitors and risk of colorectal cancer’

2021· letter· en· W3217498420 on OpenAlexaff
Devin Abrahami, Laurent Azoulay

Bibliographic record

VenueGut · 2021
Typeletter
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineProton-pump inhibitorColorectal cancerInternal medicinePropensity score matchingConfoundingIncidence (geometry)PopulationCancerOncologyEnvironmental health

Abstract

fetched live from OpenAlex

We thank Chen Dong et al 1 for their interest in our study on the use of proton pump inhibitors (PPIs) and colorectal cancer incidence.2 We have responded to their comments as follows. While there may be some differences between PPI and histamine-2 receptor antagonist (H2RA) users, these two drug classes are used across similar indications in the real-world setting.3 Thus, the use of H2RAs as an active comparator provides a clinically meaningful comparison for physicians deciding whether to treat patients with gastric disorders with a PPI or H2RA. This is certainly a better approach than comparing PPI users to non-users. Furthermore, we included all approved and off-label indications for acid suppressant drug use in our propensity score models to minimise residual confounding by disease severity. After applying propensity score weights, our population was well balanced on all …

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.279
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.283
Teacher spread0.272 · 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 designNot applicable
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

Citations1
Published2021
Admission routes1
Has abstractyes

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