Appendix mass: do we know how to treat it?
Bibliographic record
Abstract
INTRODUCTION: The traditional management of appendiceal mass has been an initial conservative approach followed by interval appendicectomy. More recently, the necessity of interval appendicectomy has been questioned by a growing amount of evidence in the surgical literature. The aim of this study was to review the available scientific evidence and to determine how appendiceal masses are currently being managed in the Mid-Trent region by general surgeons. PATIENTS & METHODS: A literature search using Medline, Embase, Cinahl, HMIC and Biosis was carried out. A personal or telephonic survey of all consultants and specialist registrars working in general surgery in the Mid-Trent region (n = 67) was conducted recording their management protocol of 3 different clinical scenarios--a 14-year-old boy, a 29-year-old female and a 68-year-old male. Responses of the questionnaire were entered to a database in Microsoft Access 2000 and analysed. RESULTS: The results showed that there was difference of opinion on the management of appendix mass in either scenario. Appendectomy (interval or emergency) is still practised by 75% of general surgeons in the Mid-Trent region and less that 25% manage asymptomatic appendix mass without interval appendectomy. Additionally, specialist registrars appear more likely not to offer patients interval appendicectomy after successful conservative management (P < 0.05). CONCLUSIONS: At present, there is no agreed consensus on the management of appendiceal mass. There is a need to develop a protocol for the management of this common problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".