Recall bias, pain, depression and cost in back pain patients
Bibliographic record
Abstract
OBJECTIVES: To investigate the relationship between recall bias for pain stimuli in chronic low back pain patients and the cost of managing their back pain in primary care. DESIGN: A retrospective cross-sectional investigation. METHOD: A sample of 63 low back pain patients were interviewed in a primary care setting. Information was gathered on their pain intensity, disability ratings, depression, anxiety, and duration of pain. They were also presented with a surprise recall test for pain descriptors. The cost of each patient's treatment specifically for back pain in the previous 12 months was calculated. The relationship between the cost of back pain treatment and the scores from the interview were calculated first for total cost, and then for the breakdown of individual cost items. RESULTS: Results indicated that recall bias for pain stimuli were significantly related to total cost (R2 = 8%). A detailed analysis revealed that pain intensity was related to the number of appointments with the general practitioners; depression scores related to the number of appointments with the in-house osteopaths; and recall bias for pain stimuli related to referrals to external experts (out-patients). A minority of patients high on recall bias was found to account for a disproportionate amount of the cost. CONCLUSION: Although no causal path can be deduced from the findings, the study provides a novel approach to measuring psychological factors in back pain in reference to health care utilization. It is limited by its retrospective design, and should be followed by prospective studies to understand fully the relationship between cognitive bias and utilization of health services.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".