Bibliographic record
Abstract
This book has spawned from the recent outbreak of severe acute respiratory syndrome (SARS) that devastated Hong Kong for a 4-month period during the early half of 2003. The objective of this book is to provide the reader with a quick reference guide to the different facets of this newly emerged disease. All the authors and editors of this book have ‘seen SARS in the face’ and were frontline personnel in the management of this outbreak. This book charts the experiences of clinicians, radiologists and radiographers of this new disease entity not only in Hong Kong but also in Canada. Although this book deals primarily with the radiological management of SARS providing the reader with a gamut of radiographical and high-resolution computed tomography imaging features in both adults and children, it also briefly reviews the epidemiology, clinical management and screening for SARS at the accident and emergency department. The book also discusses in detail, the infection control measures that may be required within a radiology department in order to prevent the transmission of the disease to staff and patients. SARS highlighted the role of radiology in frontline medicine and emphasized the importance of maintaining our expertise with plain radiography. The department of radiology was at the forefront of the ‘battle’ against SARS and radiographers played a leading role.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.393 | 0.225 |
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".