Assessing the utility of a patient-reported screener question to detect fatigue symptoms: Improving the quality of systematic symptom measurement in clinical practice.
Bibliographic record
Abstract
236 Background: In Ontario, there is a concerted effort to screen all cancer outpatients for clinically significant symptoms at every visit, without causing undue burden on the patient. Although fatigue symptoms are common in cancer patients, severe fatigue may require clinical intervention. To reduce reporting fatigue, we evaluated the use of a single item screener question to detect severe fatigue, as defined through the FACT-Fatigue Scale (FACT-F), with the goal of reducing patient reporting burden. Methods: 316 Princess Margaret Cancer Centre outpatients across a wide range of cancers at all phases of therapies and disease stages were asked to report fatigue symptoms using the FACT-F. The ability of one screener question “I feel fatigued” to detect severe fatigue symptoms in any of the six other fatigue-related questions was evaluated. Using the presence of any severe fatigue symptom as the reference, sensitivity (Se) and specificity (Sp) of the screener question was determined. Results: Median age was 59 (19-91) years; 45% were male. The prevalence of significant, severe fatigue-related symptoms for the six individual questions covering various fatigue domains ranged from 4% to 7%. 12% of patients exhibited at least one severe symptom on FACT-F (prevalence). Defining a positive screen as “quite a bit” or “very much” fatigued, with 16% prevalence, the screener question was able to correctly identify any severe symptom 81% of the time (Se) and was able to rule out any severe symptoms 92% of the time (Sp). Conclusions: The use of a screener question to accurately detect patient symptoms provide patients with the ability to be involved in their care without being overly burdened in the process. At the meeting we will provide updated results on 500 patients, the potential modifying role of clinico-demographic factors, and results of the performance two additional screener questions on fatigue. While patient-reported outcomes are widely used in research, they may also be a practical and acceptable means to accurately detect clinically important symptoms in the clinic.
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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 | 0.198 | 0.391 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".