Does an enhanced recovery programme affect readmission rates following colorectal resection?
Bibliographic record
Abstract
Background: Young women are at a very low risk of breast malignancy, and breast lumps are often proven to be benign, such as fibroadenomas.We aimed to observe the final diagnosis of women aged 25 and under presenting with a breast lump with benign ultrasonic appearances suggestive of fibroadenoma, and assess whether biopsy is strictly necessary.Methods: Details of all women aged 25 and under attending a district general hospital with a breast lump between 2005 and 2010 was obtained.Histopathological reports were then correlated with those patients who had ultrasound scans which were suggestive of fibroadenoma.Results: 324 scans were performed in the 5 year period.86 (26.5%) were reported to have benign appearances suggestive of a fibroadenoma.Of these 56 (65%) patients subsequently underwent FNA (fine needle aspiration), 1 (1.1%) proceeded straight to core biopsy and 10 (11.6%) straight to excision biopsy.In all cases the histopathological diagnosis was that of a fibroadenoma.Conclusions: Ultrasound is a reliable tool to diagnose fibroadenomas.In the low risk group aged 25 and under, we propose that when ultrasound features are highly suggestive of a fibroadenoma, and when the lump is not causing any significant symptoms, biopsy is not necessary.0332
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".