Twenty-Four Years’ Experience With a Pulmonary Pathology Journal Club: What Have We Learned?
Bibliographic record
Abstract
CONTEXT.—: A monthly pathology journal club has met for 24 years. It was established to help members stay apprised of the literature relevant to diagnostic pulmonary pathology. OBJECTIVE.—: To assess whether the journal club met its goal and to report on opportunities identified for improvement. DESIGN.—: To determine whether articles chosen for discussion as opposed to notation were more significant, Scopus citation indices for article types reviewed from January 2007 to November 2023 were compared. A survey of current faculty was undertaken to determine if the club was meeting expectations and to identify improvement opportunities. RESULTS.—: Articles from January 2007 to November 2023 included 858 discussed and 3385 noted. Mean (SD) citation count was 103.0 (409.80) for discussion and 64.9 (259.77) for notation articles (P < .001). The citation count was noticeably right skewed, as articles with high citation counts inflated the mean. Members were mostly satisfied with the way the journal club was structured and managed. Members most valued the summary of the articles, followed by the live discussion. Opportunities for improvement included decreasing the number of journals scanned, decreasing detail in summaries, and using generative artificial intelligence (AI) to facilitate summary generation. A pilot using AI anecdotally reduced preparatory time, but the human-edited summary included more specific context, critical commentary, and enhanced take-home messages, providing a more nuanced analysis. CONCLUSIONS.—: The journal club met its initial goal. Opportunities for improvement have been identified including the use of generative AI to facilitate article summarization.
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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".