Determining the Impact of Journal Abstract Structure and Word Limit on the Completeness of Study Reporting [32G]
Bibliographic record
Abstract
INTRODUCTION: It is vital that journal abstracts, which summarize clinical research, contain sufficient information on the study, for readers to draw appropriate conclusions. Our study examined the influence of variations in abstract structure and maximum word limit among obstetrics and gynecology (OBGYN) journals on the completeness of study reporting. METHODS: We conducted a retrospective study examining 163 abstracts from 50 OBGYN journals using a modified, 32-item, version of a previously-published checklist for abstract assessment. Abstracts were classified into three groups based on abstract structure – unstructured, structured-with-headings and structured-without-headings, and three based on word count – less-than-250, 250- and greater-than-250 words. Based on preliminary assessment of abstracts, which scored 37-78% on the checklist, we determined that we needed 140 abstracts to determine differences between groups, with 80% power. Primary outcome was abstract quality score. ANOVA and linear regression analysis were used to determine whether abstract structures and word limits were associated with journal abstract quality. RESULTS: A significant difference in mean abstract scores were found for word limit (P=.012) but not abstract structure (P>.05). Mean score of the greater-than-250-word and 250-word category were significantly higher than those of the less-than-250-word category (68.25 vs 59.69, P=.003 and 65.20 vs 59.69, P=.046 respectively). CONCLUSION: Although abstract structure does not seem to influence quality, OBGYN journal abstracts with less-than-250-words might exclude important study information. Research on other factors influencing abstract quality could help journals improve the appropriate condensation of study information into abstracts, which are most widely read and often used to guide busy clinicians.
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 | 0.305 | 0.675 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".