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Record W2151328040 · doi:10.1136/ebn.9.2.36

Reporting of weighted event rates in<i>Evidence-Based Nursing</i>abstracts of systematic reviews

2006· article· en· W2151328040 on OpenAlexaff
Stephen R. Werre

Bibliographic record

VenueEvidence-Based Nursing · 2006
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIntervention (counseling)HarmEvent (particle physics)PlaceboTable (database)Family medicineAlternative medicineData miningNursingPsychologyComputer sciencePathology

Abstract

fetched live from OpenAlex

E vidence-Based Nursing publishes many abstracts of systematic reviews. In these abstracts, we try to the report the results in a consistent format—ie, in a table that includes weighted event rates for intervention and control groups, relative risk reductions (or increases), and numbers needed to treat (or to harm). If you have ever tried to check these calculations using the data reported in the original review, you may have puzzled over our reporting of intervention event rates. Take a look, for example, at the abstract and table of the systematic review by Gafter-Gvili et al 1,2 on p50. This review assessed the effectiveness of antibiotic prophylaxis for patients with neutropenia. One of the analyses compared the effects of fluoroquinolones (intervention) and placebo or no intervention (control) on infection related mortality. In our abstract table, you will see that 1.9% of patients who received fluoroquinolones had deaths related to infection compared with 6.9% of patients who received placebo or no intervention. The data from the original review are shown below in figures 1⇓ and 2⇓. You can quickly add up the number of events for each group and divide by the total number of patients: the event rate for the control group is 33/480 or 6.9%, same as in the abstract table; the event rate for the fluoroquinolone group is 14/542 or 2.6%—different from the 1.9% reported in the abstract table. Is this a typo or did someone make an error? The answer is …

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
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 head0.113
GPT teacher head0.390
Teacher spread0.277 · 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 designSystematic review
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
Published2006
Admission routes1
Has abstractyes

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