FROM MY PT SCHOOL NOTES · EVIDENCE-BASED PHYSICAL THERAPY · CHAPTER 9
What Can a Prognosis Study Really Predict?
A prognosis study estimates likely future outcomes in groups. It cannot tell one individual precisely what will happen.
Define the outcome and timeframe
Prognosis considers possible future outcomes, the probability of those outcomes and the timeframe. The lecture connects this to function, assistive-device needs, return to activity, discharge destination, prevention, and anticipating a condition’s course.
Study terms: prognostic factor or indicator is a characteristic that may predict an outcome; a risk factor typically indicates a greater likelihood of an adverse outcome. The chapter discusses inception cohorts, appropriate follow-up, clear operational definitions, missing participants, masking of outcome assessors, relevant subgroups and independent confirmation of results.
Read the cohort and follow-up
The lecture covers cohort methods, survival curves and statistical estimates including odds ratios (OR), risk ratios (RR) and confidence intervals. A ratio above 1 or below 1 must be interpreted with the specified event, comparison group and interval. A confidence interval spanning 1 suggests the null ratio is compatible with the data under the model.
Apply a population estimate to one person
The practical question is whether the participants and setting resemble the patient, and whether the estimated outcome is important enough to affect shared planning. Even a strong group-level prognostic relationship cannot determine one person’s future with certainty.
Check your reasoning: Does a ‘95% follow-up’ label alone prove a prognosis study is unbiased?
No. Reasons for missing follow-up, timing, population differences and missing-data handling still matter.
Updated-standard note
The old lecture states that 5% loss to follow-up has little effect while 20% undermines validity. Attrition bias depends on why participants were lost, their characteristics and missing-data handling, not one universal percentage. The slides also incorrectly describe a p-value and a 95% confidence interval in simple probability terms; compare with your own statistics course and current appraisal resources.
Put it into practice
Scenario: A prognosis study states that 70% of participants were back to a chosen activity at six months. A substantial number of patients were not followed up.
What should you conclude?
Reasoning: Before applying the 70% estimate, ask when recruitment occurred, who was lost, how outcomes were defined and whether those missing differ from those retained. The apparent estimate may be biased.
Common trap: Treating a group outcome rate as an individual prediction or ignoring attrition. Remember: Prognosis is a probabilistic, time-specific estimate.
Course basis: my Evidence-Based PT school notes, source lecture Chapter 12. New questions and headings are teaching aids, not official exam items. For full definitions and examples, use the class notes and assigned readings.
