Test of Memory Malingering (TOMM) Scores are not Affected by Chronic Pain or Depression in Patients with Fibromyalgia
Bibliographic record
Abstract
Neuropsychologists routinely give effort tests, such as the Test of Memory Malingering (TOMM). When a person fails one of these tests, the clinician must try to determine whether the poor performance was due to suboptimal effort or to chronic pain, depression, or other problems. Participants were 54 community-dwelling patients who met American College of Rheumatology criteria for fibromyalgia (FM). In addition to the TOMM, they completed the Beck Depression Inventory-Second Edition, Multidimensional Pain Inventory-Version 1, Oswestry Disability Index-2.0, British Columbia Cognitive Complaints Inventory, and the Fibromyalgia Impact Questionnaire. The majority endorsed at least mild levels of depressive symptoms (72%), and 22% endorsed "severe" levels of depression. The average scores on the TOMM were 48.8 (SD = 1.9, range = 40-50) for Trial 1, 49.8 (SD = 0.5, range = 48-50) for Trial 2, and 49.6 (SD = 0.9, range = 45-50) for Retention. Despite relatively high levels of self-reported depression, chronic pain, and disability, not a single patient failed the TOMM. In this study, the TOMM was not affected by chronic pain, depression, or both.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".