Surgical management of neuroma pain: A prospective follow-up study
Bibliographic record
Abstract
Painful neuromas can cause severe loss of function and have great impact on the daily life of patients. Surgical management remains challenging; despite improving techniques, success rates are low. To accurately study the success of surgical neuroma treatment and factors predictive of outcome, a prospective follow-up study was performed. Between 2006 and 2009, pre- and post-operative questionnaires regarding pain (VAS, McGill), function (DASH), quality of life (SF-36), symptoms of psychopathology (SCL-90), epidemiologic determinants and other outcome factors were sent to patients surgically treated for upper extremity neuroma pain. Pain scores after diagnostic nerve blocks were documented at the outpatient clinic before surgery. Thirty-four patients were included, with an average follow up time of 22 months. The mean VAS score decreased from 6.8 to 4.9 after surgery (p<0.01), 19 (56%) of patients were satisfied with surgical results. Upper extremity function improved significantly (p=0.001). Neuroma patients had significantly lower quality of life compared to a normal population. Employment status, duration of pain and CRPS symptoms were found to be prognostic factors. VAS scores after diagnostic nerve block were predictive of post-operative VAS scores (p=0.001). Furthermore, smoking was significantly related to worse outcome (relative risk: 2.10). The results could lead to improved patient selection and treatment strategies. If a diagnostic nerve block is ineffective in relieving pain, patients will most likely not benefit from surgical treatment. Patients should be encouraged to focus on activity and employment instead of their symptoms. Smoking should be discouraged in patients who will undergo surgical neuroma treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".