Fu's subcutaneous needling for subcutaneous adhesions and scar hyperplasia in the neck region
Bibliographic record
Abstract
RATIONALE: Lymphadenectomy for tongue cancer in the neck region is often accompanied by local impaired mobility, gland damage, difficult in swallowing, and postoperative complication and seriously affects patients life quality. We reported a case of subcutaneous adhesions and scar hyperplasia in the neck region after lymphadenectomy for tongue lesions accompanied by impaired neck mobility and difficult in swallowing was treated using Fu's subcutaneous needling (FSN) treatment. PATIENT CONCERNS: A 55-year-old male with tongue cancer received surgical intervention with lymphadenectomy 8 years ago was revealed a 15 cm-long curved surgical incision in the neck region and surrounded by numerous scar tissues. DIAGNOSIS: Post-operation subcutaneous adhesions and scar hyperplasia in the neck region after lymphadenectomy was diagnosed. INTERVENTIONS: FSN treatment was performed 2 to 3 times per week for 1 month to sway the affected tightened muscle and dissociate the superficial fascia beneath the scar resulted in a considerable improvement in neck movement. OUTCOMES: The Vancouver Scar Scale (VSS) was as follows: color (M) - 1; vascular distribution (V) - 0, thickness (H) - 2, and flexibility (P) - 4, with a total of 7 points before FSN treatment. The VSS after 1 month of FSN treatment was as follows: M1, V0, H2, and P2, with a total of 5 points. Neck mobility in different directions, i.e., stretching to the back of the neck and laterally bending the neck to the left and/or right side, was improved (P < .05). LESSONS: At present, treatment of chronic scar hyperplasia has certain side effects and limitations. FSN is safe and convenient, with minimal destruction of the superficial fascia, having evident effects of dissociating tissue adhesion under scars and compensating for deficiencies in scar hyperplasia treatment. It can provide new ideas for future treatments.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".