Healthcare disparities in pituitary surgery: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Pituitary surgery is a frequent neurosurgical procedure for the management of pituitary adenomas, but little research has been done on the impact of healthcare disparities on surgical results. Healthcare inequity/disparity in terms of race and socioeconomic status (SES), in addition to age and gender, was evaluated in this study to see if they affect the results of pituitary surgery. METHODS: A systematic literature search was carried out utilizing the MEDLINE (PubMed), Web of Science, Scopus, and Embase electronic databases from conception to 2023. The Newcastle-Ottawa Scale was used for quality assessment of the included studies. RESULTS: Twenty-one studies yielded a total of 381,643 patients, and removal of the studies with temporal overlap resulted in 134,832 patients with a mean ± SD age of 51.52 ± 0.41 years. Based on the available data, 46.63% of patients were male. Black patients were more likely to be recommended against surgery, while Asian or Pacific Islander patients were more likely to be recommended for surgery. Postoperative course and outcome showed mixed results, with some studies reporting higher rates of transient diabetes insipidus and stroke in racial minority populations. Private hospitals admitted more White patients, and certain racial groups had reduced access to high-volume centers. SES disparities were assessed in terms of insurance and income. Patients with government insurance or without insurance were more likely to be recommended active surveillance instead of definitive treatment. Furthermore, high SES was associated with a higher likelihood of receiving surgical treatment, better treatment outcomes, and better access to high-volume centers. In terms of age and gender disparity, older patients and females were less likely to be recommended for surgical treatment. Age and gender did not consistently impact postoperative course and treatment outcomes, with varying results across studies. No significant age and gender disparities were observed in hospital admissions and charges. CONCLUSIONS: This study revealed the presence of disparities in pituitary adenoma surgery based on race, SES, age, and gender. These disparities highlight the need for further research and interventions to ensure equitable access to appropriate surgical treatment and improved outcomes for all patients with pituitary adenomas.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".