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Record W2125147842 · doi:10.1093/cid/cit041

Reply to Trezza et al

2013· letter· fr· W2125147842 on OpenAlexaff
Francesco Lapi, Pierre Ernst, Samy Suissa

Bibliographic record

VenueClinical Infectious Diseases · 2013
Typeletter
Languagefr
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Trezza et al [1] raise 2 methodological issues regarding our observational study on serious arrhythmia associated with fluoroquinolones [2]. First, Trezza and coworkers point out that our study did not consider exposure to fluoroquinolones during hospitalization, notably because the databases we used included only data on outpatient prescriptions, none on inpatient medication use. Indeed, as correctly noted, fluoroquinolones may be administered in hospital (eg, for chronic obstructive pulmonary disease exacerbations) and, given the acute nature of arrhythmias, they can contribute to the development of the event during hospitalization. This is precisely why, within the limits of this study, we considered any hospitalization during the current exposure time window (the 14-day period prior to hospitalization for the arrhythmia event or the corresponding index date for the controls) to be a potential source of missing exposure and immeasurable time bias [3]. A total of 7.3% of the cases vs 1.5% of the controls had been hospitalized during this exposure time window. As Trezza et al suggest, not accounting for missing exposure information during these hospitalizations could bias the findings, leading to an underestimation of the risk. This is precisely what our study found. Indeed, we observed no or attenuated associations when these periods of hospitalization were not taken into consideration, thus assuming that patients were not exposed during their stay. However, when we excluded these individuals hospitalized during the current time window, as a way to adjust for immeasurable exposure, the risk was increased. In other words, in the ideal situation where we could have measured in-hospital exposure to fluoroquinolones, our point estimates would be even greater. This effect can be expected to be higher still from our use of a cohort of users of respiratory medications, for whom the use of in-hospital fluoroquinolones should be higher than in the general population [4, 5]. Second, it is quite unlikely that in-hospital unmeasured confounders can explain the magnitude of risks we found to be associated with the use of fluoroquinolones (the lowest rate ratio was 2.15 for ciprofloxacin). First, our study already adjusted for several confounders, including established risk factors for arrhythmia. Second, while factors such as electrolyte imbalances, ischemia, inflammation, and hypokalemia that often occur during hospitalizations are indeed risk factors for arrhythmia, they must be so above and beyond the already adjusted-for factors. Moreover, it is not evident that they are also associated with the use or choice of fluoroquinolones, an essential second condition for confounding to occur. In all, Trezza et al bring up important points which, within the realm of our study, would suggest that the risks we found are in fact underestimates of the true risks. Their letter also highlights the need for studies using hospital databases that include inpatient drug exposures. Financial support. P. E. has received institutional grant funding from the Drug Safety and Effectiveness Network, Canadian Institutes of Health Research. Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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)0.006
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.054
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.004
Open science0.0030.001
Research integrity0.0540.035
Insufficient payload (model declined to judge)0.0070.007

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 head0.047
GPT teacher head0.399
Teacher spread0.352 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2013
Admission routes1
Has abstractyes

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