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Paper Triage in 7 Minutes: Decide If It’s Worth Your Time

A time-boxed way to triage papers: abstract-first reading, methods sniff-test, results extraction, and a “trust and relevance” score you can apply on shift.

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Key points

  • You are not reading to be impressed — you are reading to decide.
  • Triage is a filter: relevance first, then trust, then applicability.
  • Most papers can be rejected quickly for the right reasons (wrong population, wrong outcome, weak design, biased reporting).

The 7-minute triage clock

1

Minute 1 — Title + last sentence of the abstract

Identify what the authors claim. If the claim is irrelevant to your question, stop immediately.
2

Minutes 2–3 — Population, design, outcomes (from the abstract)

Extract: who was studied, what was done, and what was measured. If the outcome is a proxy you don’t care about, be cautious.
3

Minutes 4–5 — Methods sniff-test

Look for allocation, comparators, follow-up, missing data handling, and whether outcomes were pre-specified. You are detecting obvious threats to validity.
4

Minute 6 — Results extraction

Find the actual effect size (not just p-values). If you cannot find an effect size quickly, that is itself a signal.
5

Minute 7 — Trust × Relevance score

Give a quick 1–5 for trust (design + bias risk) and 1–5 for relevance (population + outcome match). Anything <3 in either category needs a reason to proceed.

The point of triage

Triage protects your attention. It prevents you from spending 45 minutes on something that was never going to change your decision.

Fast validity red flags

  • No clear comparator (or comparator is not clinically meaningful).
  • Outcome switching or unclear primary outcome.
  • Heavy reliance on surrogate outcomes without justification.
  • Large loss to follow-up without transparent handling.
  • Effect sizes missing or buried; reliance on “significant/non-significant” language.
  • Conflicts of interest not disclosed clearly.
SourceBMJ: How to read a paper (series hub)
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  • Identify what the authors claim. If the claim is irrelevant to your question, stop immediately.

    Return to this section
  • Extract: who was studied, what was done, and what was measured. If the outcome is a proxy you don’t care about, be cautious.

    Return to this section
  • Look for allocation, comparators, follow-up, missing data handling, and whether outcomes were pre-specified. You are detecting obvious threats to validity.

    Return to this section
  • Find the actual effect size (not just p-values). If you cannot find an effect size quickly, that is itself a signal.

    Return to this section
  • Give a quick 1–5 for trust (design + bias risk) and 1–5 for relevance (population + outcome match). Anything <3 in either category needs a reason to proceed.

    Return to this section
  • Return to this section
  • Return to this section
  • Return to this section
  • Return to this section
  • Return to this section
  • Return to this section

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