Clinical Innovation Poster Abstracts
Bibliographic record
Abstract
Patient: An 18-year-old male with no significant past medical history presented with phantom limb pain secondary to a recent right-sided trans-tibial amputation.Case Description: Due to complications from a motorcycle collision, the patient underwent a traumatic trans-tibial amputation and T4-T9 posterior spinal fusion (PSF). He was admitted to inpatient rehabilitation for pain control and mobility needs. The patient initially reported persistent 6/10 mid-back pain and 8/10 right-sided phantom limb pain, despite a multimodal analgesic regimen consisting of acetaminophen, gabapentin, oxycodone, heat therapy, and desensitization therapy.Assessment/Results: The patient was started on a trial of memantine 5mg BID and found significant relief one day after initiation. He denied all phantom limb pain for the remainder of his two-week inpatient rehab stay. The patient’s total oxycodone requirements were gradually weaned; however, he did still require intermittent spot doses for his mid-back pain. He did not report any immediate side effects related to memantine.Discussion: Memantine is an NMDA receptor antagonist that may help in limiting neuronal excitation and abnormal sensory pain manifestations. Due to its prolonged tolerability, low side-effect profile, and relatively rapid onset, its popularity as an off-label adjunct in chronic pain treatment has grown. Literature regarding memantine for phantom limb pain is limited.Conclusion: Memantine may be an effective modality for treating phantom limb pain and limiting the need for higher-risk alternative analgesics during hospitalizations. Further studies are needed to determine the role of memantine in the treatment of neuropathic pain syndromes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.720 | 0.244 |
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".