Gossypiboma: A case study of medical error in obstetrician Tertiary Care Hospital.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pakistan is striving hard to achieve millennial developmental goals by considering multiple factors. However, maternal mortality and morbidity due to medical errors remain unnoticed and undocumented due to lack of reporting system. This case report is based on a multigravida, who presented with severe abdominal pain and tenderness. She was on multiple medications after five months of three consecutive surgeries including initial surgery for uterine rupture during labor. On examination, a mass was noticed in the umbilical region. A foreign body was suspected on ultrasound and diagnosed as gossypiboma after surgery. It is usually misdiagnosed and needs attention especially considering differential diagnosis in post-operative patients. Such errors might be avoided by properly counting number of gauze pieces before and after an intervention, and usage of radio opaque gauze pieces.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it