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Record W4324032845 · doi:10.1016/j.cjco.2023.03.007

Reply to Reiffel—Numbers Don’t Lie—But They Tell Only Part of the Story

2023· article· en· W4324032845 on OpenAlexafffund
James M. Brophy

Bibliographic record

VenueCJC Open · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsMcGill University Health Centre
FundersFonds de Recherche du Québec - Santé
KeywordsArtPsychologyMathematics

Abstract

fetched live from OpenAlex

We appreciate Reiffel's 1 interest in our paper, 2 and the opportunity it provides to reiterate important caveats that apply when interpreting research findings.First, Reiffel 1 mentions a concern with possible misclassification of diagnostic codes in administrative datasets.We agree, and we mentioned this among our limitations. 2However, this concern is mitigated by the fact that the data were generated by trained medical personnel and that these datasets have been used in over in 2700 peer-reviewed studies (https:// www.merative.com/real-world-evidence).Second, Reiffel 1 notes, again as we mentioned in the original article, 2 that propensity-score methods can adjust only for measured covariates.However, Reiffel's specific concern about the impact of beta-blockers was dealt with by exclusion, as both exposure groups were selected without any concomitant beta-blockers.Similarly, sodium-glucose cotransporter-2 (SGLT-2) medication use was not unbalanced between the groups, as these were not on the market at the time of the data acquisition.Third, the choice of research question, and as a consequence, the chosen outcome remains an essential element in any study.Reiffel 1 argues that only unplanned atrial fibrillation hospitalizations should have been considered as an outcome, but most readers would (i) find the definition of "unplanned" to be potentially arbitrary and subjective; and (ii) consider that outcome to be of lesser importance than our a priori chosen total repeat cardiovascular outcome measure.Finally, as Reiffel 1 suggests, different research questions, including the effect of drug choice on atrial fibrillation (AF) symptoms or AF burden, could have been chosen, but they quite simply were not our research question.Accordingly, we disagree with Reiffel's 1 assertion that "numbers can lie,"; rather, the issue is not with the numbers per se, but with how they are generated and interpreted.We feel that the generation and interpretation of these numbers support our conclusion that "given the large burden of disease with atrial fibrillation, a pressing need remains to reproduce and expand these research findings in different settings."

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.241
GPT teacher head0.470
Teacher spread0.228 · 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.

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
Published2023
Admission routes2
Has abstractyes

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