AN OBSERVATIONAL STUDY OF ORTHOPAEDIC ABSTRACTS AND SUBSEQUENT FULL-TEXT PUBLICATIONS
Bibliographic record
Abstract
BACKGROUND: Research abstracts are frequently referenced in orthopaedic textbooks and influence orthopaedic care. However, little is known about the quality of information provided in the abstracts, the frequency of publication of complete papers after presentation of abstracts, or any discrepancies between abstracts and published papers. The objective of this study was to determine the quality of information provided in orthopaedic abstracts, rates of publication of full-text articles after presentation of abstracts, predictors of publication of full-text articles, and consistency between abstracts and full-text articles. METHODS: We retrieved all abstracts from the 1996 scientific program of the sixty-third Annual Meeting of the American Academy of Orthopaedic Surgeons. For each abstract, we recorded the completeness of reporting and key features of the study design, conduct, analysis, and interpretation. A computerized Medline and PubMed search established whether the abstract had been followed by publication of a full-text article. Finally, we evaluated the consistency of reporting between abstracts and final publications. RESULTS: The program included 465 abstracts, 66% of which were on prognostic studies. All abstracts described the study design, and 70.7% of the designs were observational. Key methodological issues were reported in less than half of the abstracts, and information on data analysis was reported in <15%. One hundred and fifty-nine (34%) of the 465 abstracts were followed by publication of a full-text article. The mean time to publication (and standard deviation) was 17.6 +/- 12 months (range, one to fifty-six months). Inconsistencies between the abstract and the full-text article included the primary outcome measure, which differed 14% of the time, and the results, which differed 19% of the time. CONCLUSIONS: Two-thirds of the orthopaedic abstracts in this sample were not followed by publication of a full-text paper. The overall quality of reporting in abstracts proved inadequate, and inconsistencies between the final published paper and the original abstract occurred frequently. The routine use of abstracts as a guide to orthopaedic practice needs to be reconsidered.
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 | 0.099 | 0.548 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".