Establishing thresholds for important benefits considering the harms of screening interventions
Bibliographic record
Abstract
CONTEXT AND OBJECTIVE: Standards for clinical practice guidelines require explicit statements regarding how values and preferences influence recommendations. However, no cancer screening guideline has addressed the key question of what magnitude of benefit people require to undergo screening, given its harms and burdens. This article describes the development of a new method for guideline developers to address this key question in the absence of high-quality evidence from published literature. SUMMARY OF METHOD: The new method was developed and applied in the context of a recent BMJ Rapid Recommendation clinical practice guideline for colorectal cancer (CRC) screening. First, we presented the guideline panel with harms and burdens (derived from a systematic review) associated with the CRC screening tests under consideration. Second, each panel member completed surveys documenting their views of expected benefits on CRC incidence and mortality that people would require to accept the harms and burdens of screening. Third, the panel discussed results of the surveys and agreed on thresholds for benefits at which the majority of people would choose screening. During these three steps, the panel had no access to the actual benefits of the screening tests. In step four, the panel was presented with screening test benefits derived from a systematic review of clinical trials and microsimulation modelling. The thresholds derived through steps one to three were applied to these benefits, and directly informed the panel's recommendations. CONCLUSION: We present the development and application of a new, four-step method enabling incorporation of explicit and transparent judgements of values and preferences in a screening guideline. Guideline panels should establish their view regarding the magnitude of required benefit, given burdens and harms, before they review screening benefits and make their recommendations accordingly. Making informed screening decisions requires transparency in values and preferences judgements that our new method greatly facilitates.
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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 | 0.312 | 0.638 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".