Bibliographic record
Abstract
Dear Editor, We read with interest the succinct article “Radiation in medical practice and health effects of radiation: Rationale, risks, and rewards” that highlights a hidden but important issue in the healthcare system.[1] The types, problems, and key steps in the prevention of radiation hazards, that affects both patient and healthcare workers, are well described in the article. More acknowledgment and recognition of the risk posed by invisible radiation is important for creating a safer working environment. We like to add one important aspect as a corollary to the article. The widespread adoption of this initiative at the primary care level shall be critical to its success. Choosing Wisely®, a popular campaign since 2012, promotes communication between patient and caregiver with the objective to enable patient to choose a treatment that is evidence-based, truly necessary, and free from harm and duplication.[2] The campaign is supported by numerous societies or associations including the American College of Radiology (ACR) and the American Academy of Family Physicians (AAFP) among others. All supporting societies update and review a list of diagnostic and therapeutic interventions that are overused and draw recommendations based on the best available evidence. Image Wisely and Image Gently campaigns are also directed to provide safe radiation procedures in adults and children.[3] These campaigns also display radiation safety information for practitioners and patients for better decision-making. The Choosing Wisely® campaign has gathered widespread collaboration with more than 80 societies endorsing it with a collection of more than 500 recommendations apart from thousands of patient-friendly materials distributed and educative webinars conducted. Members of World Organization of Family Doctors (WONCA) also advocated its support for the campaign for its key principles against the problem of “overmedicalization” or “overdiagnosis.”[4] Recently, the National Cancer Grid of India task force recommended guidelines regarding diagnosis and treatment palliative care, diagnosis, and delivery of care to improve cancer care in India.[5] A few of these recommendations were based on Choosing Wisely USA and Canada list. Although the campaign is well received by many international fora, its pace of adoption in developing countries such as India is tardy. India has complex healthcare machinery with a gamut of heterogeneous treatment modalities and practices. Many patients get outdated, unnecessary, and unauthentic diagnostic and treatment choices. Radiation-related investigations are frequently prescribed and with proper education, the burden of these investigations in the situation where these do not bring any added knowledge or advantage on the board can be curtailed. Over the years, the quantum of radiation and its after-effects can be lessened in society by following specialty-specific recommendations. Proper and rationale prescription for common radiographs and computerized tomograms can even be done at the primary care level by studying relevant recommendations. As the primary care level is responsible for catering to a large section of society at the grass-root level, the adoption of the campaign by Indian family physicians and primary care personnel is instrumental to its success. The benefit of the campaign shall reach the common person if the Academy of Family Physicians of India (AFPI) endorses it in letter and spirit. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. Acknowledgment None.
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 | 0.010 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".