Reporting of weighted event rates in<i>Evidence-Based Nursing</i>abstracts of systematic reviews
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.319 | 0.785 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.045 | 0.038 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".