Publication Rate of Presentations at a National Physical Medicine and Rehabilitation Scientific Conference From 2009 to 2018
Bibliographic record
Abstract
ABSTRACT: Conferences are important platforms for sharing research, but full-text publication is necessary for broader dissemination and impact. Few studies have examined the abstract-to-publication rate for physical medicine and rehabilitation. This study aimed to determine the abstract-to-publication rate of research abstracts presented from 2009-2018 at the Canadian Association of Physical Medicine and Rehabilitation annual scientific meetings. Using the official book of abstracts from 2009 to 2018, year of conference, abstract title, author names, and the affiliation/level of training of the first author were extracted. Systematic searches of abstract key words, authors' names, and the abstract title were performed using PubMed and Google Scholar to determine whether an abstract proceeded to full-text publication; if so, the date and journal of publication were extracted. Of the 524 total abstracts presented at the Canadian Association of Physical Medicine and Rehabilitation from 2009 to 2018, 187 went on to full-text publication for an abstract-to-publication rate of 35.8%. The mean time to full-text publication was 23.4 ± 8.63 mos. The abstract-to-publication rate was 18.6% for medical students, 28.7% for residents, 41.1% for physical medicine and rehabilitation consultants, and 49.4% for nonphysician presenters. This study highlights that low abstract-to-publication rates for novel research presented at the Canadian Association of Physical Medicine and Rehabilitation disproportionately affects trainees. Further research is needed to identify and remedy barriers to publication.
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 | 0.077 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.025 | 0.030 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".