SCIENOPS / SEEDING THOUGHTS
Better questions for the next evidence review.
Better questions for the next evidence review.
Short perspectives on scientific AI, useful uncertainty and the work of connecting evidence to decisions.
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01 / PREDICTION
A compelling prediction begins a conversation.
A compelling prediction begins a conversation.
A model can help generate or prioritise a hypothesis. The next question is what independent evidence, controls and context would justify acting on it.
Ask in the review: what would change our interpretation?
02 / TRACEABILITY
Keep the qualification beside the claim.
Keep the qualification beside the claim.
A summary can lose the detail that limits an original result. Review the source passage, analytical context and intended meaning before carrying the statement into a new document.
Ask in the review: what important caveat disappeared in the summary?
03 / APPLICABILITY
Missing and not applicable need different next steps.
Missing and not applicable need different next steps.
Missing evidence may call for another source or experiment. A method that does not fit the subject calls for a different question or approach. Treating both as a blank hides useful information.
Ask in the review: do we need more evidence or a more suitable method?
04 / REAL-WORLD DATA
More records do not remove the need for a fair comparison.
More records do not remove the need for a fair comparison.
Population definitions, treatment patterns, observation time and confounding shape the interpretation of observational evidence. Scale is one consideration among several.
Ask in the review: what assumptions make this comparison meaningful?
05 / PROGRAMME MEMORY
A stopped investigation can still create value.
A stopped investigation can still create value.
Preserve why a programme paused or changed direction, which evidence mattered and what could justify revisiting the decision. The next team should not need to reconstruct the entire discussion.
Ask in the review: what should future colleagues be able to understand?
06 / PRACTICAL VALUE
Measure the work around the answer.
Measure the work around the answer.
A useful evaluation looks at completeness, source clarity, reviewer effort and rework, as well as output quality. Choose a baseline that reflects the team’s actual workflow.
Ask in the review: what became easier to assess or do?
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