Informing Healthcare Decisions with Observational Research Assessing Causal Effect. An Official American Thoracic Society Research Statement
Bibliographic record
Abstract
Abstract Rationale Decisions in medicine are made on the basis of knowledge and reasoning, often in shared conversations with patients and families in consideration of clinical practice guideline recommendations, individual preferences, and individual goals. Observational studies can provide valuable knowledge to inform guidelines, decisions, and policy. Objectives The American Thoracic Society (ATS) created a multidisciplinary ad hoc committee to develop a research statement to clarify the role of observational studies—alongside randomized controlled trials (RCTs)—in informing clinical decisions in pulmonary, critical care, and sleep medicine. Methods The committee examined the strengths of observational studies assessing causal effects, how they complement RCTs, factors that impact observational study quality, perceptions of observational research, and, finally, the practicalities of incorporating observational research into ATS clinical practice guidelines. Measurements and Main Results There are strengths and weakness of observational studies as well as RCTs. Observational studies can provide evidence in representative and diverse patient populations. Quality observational studies should be sought in the development of ATS clinical practice guidelines, and medical decision-making in general, when 1) no RCTs are identified or RCTs are appraised as being of low- or very low-quality (replacement); 2) RCTs are of moderate quality because of indirectness, imprecision, or inconsistency, and observational studies mitigate the reason that RCT evidence was downgraded (complementary); or 3) RCTs do not provide evidence for outcomes that a guideline committee considers essential for decision-making (e.g., rare or long-term outcomes; “sequential”). Conclusions Observational studies should be considered in developing clinical practice guidelines and in making clinical decisions.
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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 | 0.765 | 0.855 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".