Bibliographic record
Abstract
The Maharashtra State Branch of the Indian Radiological and Imaging Association (MSBIRIA) and the Maharashtra College of Radiology (MCR) proudly present to you the first issue of the “Journal of Applied and Clinical Radiology.” This inaugural issue marks the beginning of a significant chapter in our shared journey to advance the field of radiology, offering a platform that supports innovation, fosters professional growth, and disseminates valuable knowledge. The journal is published at a very crucial time when the practice of radiology is undergoing a great change, with advances in technology and increasing knowledge about disease processes. The main aim of this journal is to offer a forum for radiologists, researchers, and medical students a free platform to share their original research, case reports, and reviews in radiology. It is an attempt to close the gap between academic research and clinical practice to foster evidence-based practice and ongoing professional development among members of the radiological community that improves patient care. In this inaugural issue, we are delighted to showcase articles submitted by the radiology community that challenge the edges of prevailing wisdom, such as in-depth case reports and case series with intricate clinical presentations and novel insights into the imaging findings. Every article has gone through a rigorous peer-review process to establish that it is of the highest academic integrity and clinical usefulness. The challenge of starting this new academic venture stems from keeping up with new technology and meeting the timeline for publication. Confronting these challenges through continuous learning, teamwork, and support systems, we set off on this new academic adventure with publication partner Wolters Kluwer/Medknow. The release of this journal would be impossible without giving credit to the senior radiology fraternity, Dr. Sandeep Kavthale, president elect IRIA, and all MSBIRIA members for trusting me and assigning me this herculean task of setting up this journal. Dr. Pradeep Gandhi, Dr. Pravin Sagole, Prof. Dr. Shailesh Sangani, and the MSBIRIA and MCR teams to actively encourage the launch of this new academic platform for the radiologists. It needs a lot of effort and complete dedication from day 1 of ideating the journal to narrowing its scope and standard and making this dream a reality. It is through the commitment of authors, reviewers, members of the editorial board, and the publication team that we were able to bypass these obstacles and make this dream a reality. In addition, we understand that the success of this journal is dependent on ongoing improvement. Going forward, we are committed to making the processes and content of the journal better, guided by your feedback and needs as readers. Your participation is important, and we invite you to read and gain from the research shared and contribute your work and thoughts. As we begin this endeavor, I offer our sincerest thanks to all who have worked towards making this vision a reality. If I have inadvertently forgotten to mention anyone, then it must be regarded as my fault, and I wholeheartedly apologize. We invite you to engage actively with this publication, share your perspectives, and participate in shaping a future where radiology continues to grow and thrive. Together, let us overcome challenges, support one another, and drive the field toward greater achievements. With best wishes, Dr. Anand Hatgaonkar Editor in Chief, JAACR, Professor & HOD Radiodiagnosis, Datta Meghe Medical College, Nagpur.
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 | 0.040 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.045 | 0.048 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.024 | 0.042 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".