Making Emergency Surgical Decisions Without any Imaging Evidence: A Case Report of Complicated Diverticular Phlegmon
Bibliographic record
Abstract
Acute left-sided diverticulitis is the third most common gastrointestinal disease after acute pancreatitis and cholecystitis requiring hospitalization. From those patients, 15% to 20% were diagnosed with abscess on the computed tomography (CT) scan. Usually, abscess larger than 5 cm are not amenable for medical treatment. A 61-year-old woman presented to emergency department of the general hospital in the remote island with 48-h history of fever, tachypnea, and tachycardia. Physical examination revealed 15 × 7 cm mass occupying the left mid-abdomen and iliac fossa. Patient did not report any unintentional loss of weight or change of bowel habits. She only reported that the last month she felt her lower tummy bloated. Due to absence of radiographer during this period in the hospital there was no possibility for any imaging investigations. Diagnostic laparoscopy revealed a phlegmon in the left abdomen consisting of the sigmoid colon, loops of the small bowel and wrapped by the omentum. Hartmann procedure was performed. Patient recovered uneventfully and was scheduled for reversal procedure. Surgical intervention is the treatment of choice for complicated large diverticular abscess; in the remote island, any delayed diagnosis may lead to life-threatening complications.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| 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".