Five‐year publication rate of clinical presentations at the open and closed American shoulder and elbow surgeons annual meeting from 2005–2010
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to evaluate the five-year publication rate of papers presented at both the open and closed American Shoulder and Elbow Surgeons' (ASES) annual meetings from 2005 to 2010. METHODS: Online abstracts of the presentations at the open and closed ASES annual meetings were independently screened for clinical studies and graded for quality using level of evidence. The databases PubMed (MEDLINE), Ovid (MEDLINE), and EMBASE were comprehensively searched for full-text publications corresponding to these presentations and any paper published within five years of the presentation date was counted. RESULTS: Overall, 131/266 papers corresponding to the meeting presentations were identified for a five-year publication rate of 49.2 %. Sixty two (48 %) of the papers were published in The Journal of Shoulder and Elbow Surgeons, 23 (18 %) were published in The American Journal of Sports Medicine, and 20 (16 %) were published in The Journal of Bone and Joint Surgery. The mean patient sample size included in presentations with a subsequent full-text publication was higher (154; standard error =27) than the presentations not published (93; standard error = 13) (p = 0.039). There was no correlation (p = 0.248) between the publication rate and the level of evidence of the presentations. CONCLUSIONS: The publication rate of presentations at ASES meetings from 2005 to 2010 is similar to that reported from other orthopaedic meetings. Studies with large sample sizes should continue to be encouraged, and high quality presentations must consistently be followed up with full-text manuscript preparation in order to maximize the future clinical impact.
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 | 0.054 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".