Surgeon identification of pain catastrophizing versus the Pain Catastrophizing Scale in orthopedic patients after routine surgical consultation
Bibliographic record
Abstract
Background: A high level of pain catastrophizing has negative influences on outcomes in many surgical disciplines. Our purpose was to determine whether surgeons are able to accurately identify high catastrophizing in orthopedic patients after routine clinical consultation. Methods: In this prospective study, English-literate patients aged 18 years or older were assessed by 1 of 11 orthopedic surgeons. Patients completed the Pain Catastrophizing Scale (PCS), and the surgeon rated each patient as having a high or low level of catastrophizing after the clinical encounter. We calculated accuracy and agreement of surgeon assessment with the PCS at a cut-off score of 30 (score ≥ 30 = high level of catastrophizing) and used multivariate testing to determine whether patient age or sex, surgeon experience or subscores of the PCS (rumination, magnification and helplessness) influenced surgeon accuracy. Results: Among 203 patients (109 women and 94 men), the mean PCS score was 18.4 (standard deviation 12.9), with no sex difference and no significant correlation to patient age. Of the 40 patients who scored 30 or more on the PCS, 22 (55%) were not identified as having high levels of catastrophizing by their surgeon. Accuracy was 0.72, and agreement was 0.2. Female patients were more likely than male patients to be identified as high catastrophizing regardless of PCS score (odds ratio 2.0, 95% confidence interval 1.04–4.0). Conclusion: Surgeons were not able to accurately identify patients with high levels of pain catastrophizing during routine initial consultation. In considering which patients may most benefit from interventions to improve coping and reduce catastrophizing, explicitly measuring pain catastrophizing will be required.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".