Providing clinical guidance in the middle of a global pandemic: Caveats and opportunities
Bibliographic record
Abstract
The Canadian Thoracic Society (CTS) has long been recognized for its high-quality clinical practice guidelines. Over the years, as recommended methods for guideline production have evolved, the CTS guideline process has become more rigorous and lengthier. In the context of the COVID-19 pandemic, this model was challenged by the urgent need for guidance, insufficient time for conventional guideline processes, and shrinking human resources and capacity. Accordingly, the CTS pivoted from guidelines to the more rapid, narrative, and informal “position statement” format. Having produced 17 COVID-related statements, the CTS saw its guideline website visits increase by 90% and downloads by 63% in 2020 versus 2019. However, providing rapid guidance in the form of position statements necessitates a significant tradeoff in the rigor of methodological processes used to arrive at recommendations. Previous research suggests that robust “rapid guidelines” may be feasible through modifications to conventional guideline development processes. Such approaches were successfully implemented by the Infectious Diseases Society of America (IDSA), the World Health Organization, and the UK’s National Institute for Health and Care Excellence (NICE) for COVID–19–related guidelines. Rapid guidelines have been made more feasible by better information sharing and advancing technologies such as an online COVID network meta-analysis engine and an app that allows organizations to author, publish and update digital guidelines. The CTS must continue to produce urgent guidance for decision makers, providers and the public alike in the context of this pandemic and should explore opportunities to espouse a standardized, rigorous and transparent process for rapid guidelines.
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 | 0.362 | 0.645 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.017 | 0.050 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".