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Record W2498661651 · doi:10.1089/gyn.2016.0009

Should We Manage Large Ovarian Cysts Laparoscopically?

2016· article· en· W2498661651 on OpenAlexaboutno aff
Gaurav Chopade, Saurabh Patil, Tanuka Das, M. Thomas, Reena Garg

Bibliographic record

VenueJournal of Gynecologic Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLaparoscopySurgeryGeneral surgeryOvarian tissueOvaryGynecologyInternal medicine

Abstract

fetched live from OpenAlex

Objective: The aim of this research was to evaluate the feasibility and surgical outcomes of laparoscopic surgery for large ovarian cysts in women <40 years of age. Materials and Methods: This was a retrospective evaluation (Canadian Task Force classification 11-2 design) of 55 women (ages <40) with large ovarian cysts (≥10 cm) with features suggestive of benign disease managed laparoscopically at Paul's Hospital, in Cochin, Kerala, from July 2006 to April 2013. All patients were followed-up for a minimum of 1 year. Patients who were diagnosed as having borderline ovarian tumors were evaluated for their present clinical status at the end of study. Results: Laparoscopic surgery was performed successfully for all patients. The mean operative time, estimated blood loss, and hospital stay were, respectively, 109.6 minutes (range: 40–255), 304.6 mL (range: 100–650), and 1.1 days (range: 1–3). Conversion to laparotomy was performed in none of the patients. Five cases of borderline malignancy were detected. Of these 5 cases; 3 underwent laparoscopic adnexectomy; 1 underwent bilateral cystectomy with staging biopsies, conceived 3 months postsurgery, and subsequently underwent laparoscopic adnexectomy at another center; and 1 underwent a unilateral laparoscopic cystectomy, and had a laparotomy and adnexectomy in another institution after 1 month. Conclusions: The current study supports laparoscopic management of large ovarian cysts as a technically feasible and effective method if proper case selection is applied. (J GYNECOL SURG 32:251)

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.055
GPT teacher head0.303
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2016
Admission routes1
Has abstractyes

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