Low back pain as main symptom in Low-grade Appendiceal Mucinous Neoplasm (LAMN): A case report
Bibliographic record
Abstract
BACKGROUND: Low back pain is the leading cause of disability worldwide. It is also the main cause of the limitation of activities and absence from work in much of the world and a cause of great economic burden. The greatest percentage of low back pain is classified as nonspecific (i.e. not attributable to a defined pathology), while the others may concern specific pathologies of the lumbar region or suggest pathologies of non-musculoskeletal origin. Consequently, evaluating any signs and symptoms mimicking musculoskeletal conditions is crucial. CASE DESCRIPTION: This case report describes a 64-year-old female patient who first presented to the physiotherapist with two weeks of low back pain, complaining of a different clinical presentation than the previous episodes. The patient's pain started spontaneously and was located in the lumbar region over a diffuse area. Symptoms were constant throughout the day and did not change with movement. Based on the examination findings, the physiotherapist decided to make an urgent referral to the patient's General Practitioner. OUTCOMES: Following ultrasound examination and Computed Tomography scan, an 8.5 cm mass was confirmed in the abdominal region. It was surgically removed, and a histological diagnosis of Low-grade Appendiceal Mucinous Neoplasm (LAMN) was made. CONCLUSION: The physiotherapist's evaluation and decision-making process was fundamental in the patient's referral due to suspected pathology not within the scope of practice. This revealed a rare condition, which, according to the existing literature, is usually diagnosed in the event of collateral imaging findings or upon presentation of complex and/or emergency clinical pictures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| 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".