Publication of Abstracts Presented at Annual American Contact Dermatitis Society Meetings
Bibliographic record
Abstract
BACKGROUND: Medical meetings provide a forum for the open discussion and sharing of new information for attendees. Peer-reviewed publication provides greater distribution of vetted information. OBJECTIVE: The aim of this study was to analyze the publication of presentations from annual American Contact Dermatitis Society meetings. METHODS: Abstracts presented at the 2000, 2003, and 2006 American Contact Dermatitis Society meetings were identified. By keywords and author names, PubMed was searched for published papers corresponding to the work presented at these annual meetings. Matches were confirmed by comparing the content of the abstracts with the fully published articles. RESULTS: Of 115 presented abstracts, 55% resulted in the publication of an article by November 2009; 5% of abstracts had been published prior to presentation. Time to publication was 0.25 to 6.0 years. Mean time to publication was 1.8 years. CONCLUSIONS: Over one-half of all American Contact Dermatitis Society presentations resulted in the publication of an original article. Most were published within 2 years. The great majority of abstracts were published in Dermatitis (formerly the American Journal of Contact Dermatitis).
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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.015 |
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".