Phantom limb phenomenon as an example of body image distortion
Bibliographic record
Abstract
Abstract Introduction: The perception of one’s own body, its mental representation, and emotional attitude to it are the components of so-called “body image” [1]. The aim of the research was to analyse phantom pain and non-painful phantom sensations as results of limb loss and to explain them in terms of body image distortion. Material and method: Three methods were used in the study of 22 amputees (7 women and 15 men, between 43 and 76 years old, M = 61, SD = 11.3): (1) a clinical interview; (2) The Questionnaire of Body Experiencing after Limb Amputation; (3) modified version of The Pain Questionnaire based on The McGill Pain Questionnaire. Results: The prevalence of phantom limb pain was 59%. Some various non-painful phantom sensations after amputation were experienced by 77% of respondents. There was a statistically significant relationship between phantom pain and non-painful phantom sensations in a group of participants experiencing phantom limb phenomenon at the moment of the research. Conclusions: Deformation of body image in the form of phantom pain and non-painful phantom sensations is a frequent experience after limb loss. We suggest that phantom limb is a form of out-of-date or inadequate body image as an effect of the brain activity trying to keep a kind of status quo. A co-occurrence of non-painful phantom sensations and phantom pain suggests that these both forms of post-amputation sensations may share neural mechanisms. Results indicate, that there exists somatosensory memory which may be manifested in similarities between pre- and post-amputation sensations.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".