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Record W4327932220 · doi:10.1055/s-0043-1762120

Assessing Patient-Reported Symptoms to Predict Postoperative Complications from Pituitary Surgery Using a Novel Consensus-Developed Inventory

2023· article· en· W4327932220 on OpenAlexaff
Amelia K. Ramsey, Dhruv S. Kothari, Kara A. Parikh, Ralph Abi Hachem, Yvonne Chan, Garret Choby, Judd H. Fastenberg, Mindy Rabinowitz, Sanjeet V. Rangarajan

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2023
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSurgery

Abstract

fetched live from OpenAlex

Background: Endoscopic endonasal pituitary surgery is a safe and effective procedure that can often be discharged on the first or second post operative day. Published data suggests up to 8 to 10% of patients undergoing pituitary surgery can develop complications which require unscheduled visits, readmission, or reoperation, often in the 7 to 14 days following surgery prior to their first postoperative visit. During this time of increased risk, healthcare providers must utilize careful questioning and timely communication, often via phone or patient portals to determine if a patient is at risk of impending complications. We posit that patient reported symptoms can be utilized to build a predictive algorithm and reveal a patient's estimated risk following endoscopic endonasal pituitary surgery. Thus we utilized a validated expert consensus strategy to build a novel inventory to assess patient-report symptoms collected remotely following surgery. Methods: A modified Delphi technique was utilized to establish consensus amongst six rhinologists with high-volume endoscopic skull base surgery practices on the questions posed in the patient-reported symptom inventory. Experts initially submitted potential questions within the seven most common complication domains: “endocrinopathies,” “cerebrospinal fluid (CSF) leak,” “epistaxis,” “pain,” “infection,” “ophthalmologic/visual symptoms,” and “other.” Four rounds of voting discussion were required to achieve consensus on all questions, defined as at least five of six panel members agreeing to approve or remove a question from the final questionnaire. After each round, there was a discussion session conducted remotely. The final discussion occurred in-person. Results: Initially, 61 questions assessing patient-reported symptoms were proposed by and sent to experts for anonymous ranked voting. Agreement on the inclusion, content, and specific phrasing of each question was achieved via the ranked voting process and virtual/in-person discussion. Duplicate questions were eliminated prior to the first round of voting. After round 1, seven questions reached consensus for inclusion. In round 2, five questions reached consensus for inclusion. Round 3 was performed via an online meeting, and nine more questions reached consensus for inclusion. Finally, round 4 was performed via in-person discussion and eight questions reached consensus for inclusion. In total, 17 questions were included, with the remainder excluded by consensus due to similarity to other included questions, or because of other identified deficiencies. The distribution of included questions in the seven complication domains includes endocrine (4), CSF leak (3), epistaxis (2), pain control (2), postoperative infection (2), ophthalmologic (1), and other (3). Conclusion: Utilizing the modified Delphi consensus technique, our group developed a 17-question patient-reported symptom inventory which will be utilized to collect post-operative data on patients undergoing endoscopic pituitary surgery to further understand and potentially mitigate complications in the early postoperative period. Publication History Article published online: 01 February 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.144
GPT teacher head0.326
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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