Extract structured data from neurosurgical studies
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Use when extracting structured data from medical research PDFs, parsing study characteristics, patient demographics, outcomes, and results. Invoke for systematic review data collection from papers.
Use when writing systematic review manuscript sections following PRISMA 2020 guidelines. Covers abstract, introduction, methods, results, and discussion drafting for medical journals. Invoke for academic writing assistance.
Use when performing meta-analysis, pooling study data, generating forest plots, funnel plots, assessing heterogeneity, or conducting subgroup and sensitivity analyses. Invoke for any statistical synthesis of multiple studies.
Use when searching for neurosurgery literature, developing PubMed search strategies, identifying MeSH terms, or building systematic search queries. Invoke for literature searching in neurosurgery topics.
Use when assessing risk of bias or study quality. Covers RoB 2 for RCTs, Newcastle-Ottawa Scale for cohorts, ROBINS-I for non-randomized interventions, and QUADAS-2 for diagnostic studies. Invoke for quality assessment.
PRISMA-compliant manuscript drafting for systematic reviews
| name | data-extraction |
| version | 1.0.0 |
| description | Extract structured data from neurosurgical studies |
| author | NeuroResearch Agent |
| license | MIT |
| triggers | [{"pattern":"extract.*data"},{"pattern":"data extraction"},{"pattern":"extraction form"},{"pattern":"pull.*from.*study"},{"pattern":"extract.*PDF"}] |
| requires | ["filesystem"] |
| tools | [{"name":"create_extraction_form","description":"Generate extraction form template"},{"name":"extract_from_pdf","description":"Extract data from PDF study"},{"name":"validate_extraction","description":"Validate extracted data"},{"name":"compile_extractions","description":"Compile extractions into pooled dataset"}] |
| schemas | {"study_characteristics":{"study_id":"string","title":"string","authors":"array","year":"integer","journal":"string","country":"string","study_design":"enum[RCT, prospective_cohort, retrospective_cohort, case_control, case_series]","multicenter":"boolean","registration":"string"},"population":{"total_n":"integer","intervention_n":"integer","control_n":"integer","age_mean":"number","age_sd":"number","male_percent":"number","diagnosis":"string","severity":"string"},"intervention":{"name":"string","type":"enum[surgical, medical, device, combination]","details":"string","timing":"string"},"outcomes":{"binary":{"events_int":"integer","n_int":"integer","events_ctrl":"integer","n_ctrl":"integer"},"continuous":{"mean_int":"number","sd_int":"number","n_int":"integer","mean_ctrl":"number","sd_ctrl":"number","n_ctrl":"integer"}}} |
Systematic extraction of quantitative and qualitative data from neurosurgical studies following standardized schemas.
study_id: FirstAuthor_Year
title: Full title of the study
authors:
- First Author
- Second Author
- et al.
year: 2023
journal: Journal of Neurosurgery
doi: 10.3171/...
pmid: 12345678
country: USA
institution: Mayo Clinic
study_design: retrospective_cohort # RCT, prospective_cohort, retrospective_cohort, case_control, case_series
multicenter: false
centers_n: 1
study_period:
start: 2010-01-01
end: 2020-12-31
registration: NCT00000000 # if applicable
funding: NIH Grant R01...
conflicts: None declared
population:
total_n: 150
intervention_n: 75
control_n: 75
demographics:
age:
mean: 62.5
sd: 12.3
median: 63
range: [35, 85]
sex:
male_n: 90
male_percent: 60
diagnosis:
condition: Malignant MCA infarction
subtype: Right-sided
severity_scale: NIHSS
severity_mean: 18.5
severity_sd: 4.2
comorbidities:
hypertension_percent: 65
diabetes_percent: 28
smoking_percent: 35
previous_stroke_percent: 12
inclusion_criteria:
- Age 18-80 years
- MCA infarction >50% territory
- NIHSS ≥15
- Symptom onset <48 hours
exclusion_criteria:
- Bilateral infarction
- Pre-existing mRS >2
- Coagulopathy
- Terminal illness
intervention:
name: Decompressive craniectomy
type: surgical
approach: Frontotemporal
technique: Standard hemicraniectomy ≥12cm diameter
timing: <48 hours from symptom onset
additional_procedures:
- Duroplasty
- EVD placement
surgeon_experience: Senior neurosurgeons
comparator:
name: Best medical therapy
type: standard_care
details: |
- ICP monitoring
- Osmotic therapy (mannitol, hypertonic saline)
- Head elevation 30°
- Sedation as needed
- Blood pressure management
outcomes:
primary:
- name: Mortality
definition: Death from any cause
type: binary
timepoint: 6 months
intervention:
events: 15
total: 75
percent: 20.0
control:
events: 30
total: 75
percent: 40.0
effect:
measure: OR
estimate: 0.38
ci_lower: 0.18
ci_upper: 0.79
p_value: 0.009
- name: Favorable outcome
definition: mRS 0-3
type: binary
timepoint: 12 months
intervention:
events: 45
total: 75
control:
events: 28
total: 75
secondary:
- name: mRS score
definition: Modified Rankin Scale
type: continuous
timepoint: 6 months
intervention:
n: 60
mean: 3.2
sd: 1.4
median: 3
iqr: [2, 4]
control:
n: 45
mean: 4.1
sd: 1.2
- name: Length of ICU stay
type: continuous
unit: days
intervention:
n: 75
median: 12
iqr: [8, 18]
control:
n: 75
median: 10
iqr: [7, 15]
adverse_events:
- name: Surgical site infection
intervention_n: 3
intervention_percent: 4.0
- name: Hydrocephalus requiring shunt
intervention_n: 8
intervention_percent: 10.7
follow_up:
duration_months: 12
timepoints: [1, 3, 6, 12]
method: Clinic visit and phone
assessor_blinding: false
completeness_percent: 88
loss_to_followup:
n: 18
reasons:
- Death: 12
- Withdrew: 4
- Lost contact: 2
quality:
tool: Newcastle-Ottawa Scale
selection:
representativeness: 1 # 0 or 1
selection_non_exposed: 1
ascertainment_exposure: 1
outcome_not_present: 1
comparability:
main_factor: 1 # Age
additional_factor: 1 # Baseline NIHSS
outcome:
assessment: 1 # Blinded
follow_up_length: 1 # ≥6 months
follow_up_adequacy: 1 # >80%
total_stars: 8
quality_rating: Good # Good (7-9), Fair (4-6), Poor (0-3)
# From 2x2 table
calc_or <- function(a, b, c, d) {
# a=events_int, b=non-events_int, c=events_ctrl, d=non-events_ctrl
or <- (a * d) / (b * c)
se_log <- sqrt(1/a + 1/b + 1/c + 1/d)
ci_lower <- exp(log(or) - 1.96 * se_log)
ci_upper <- exp(log(or) + 1.96 * se_log)
list(or=or, ci_lower=ci_lower, ci_upper=ci_upper, se_log=se_log)
}
# From events and totals
a <- events_int
b <- n_int - events_int
c <- events_ctrl
d <- n_ctrl - events_ctrl
result <- calc_or(a, b, c, d)
library(esc)
# Standardized mean difference (Hedges' g)
esc_mean_sd(
grp1m = mean_int, grp1sd = sd_int, grp1n = n_int,
grp2m = mean_ctrl, grp2sd = sd_ctrl, grp2n = n_ctrl,
es.type = "g"
)
# Mean difference
md <- mean_int - mean_ctrl
se_md <- sqrt(sd_int^2/n_int + sd_ctrl^2/n_ctrl)
# Wan et al. 2014 method
median_iqr_to_mean_sd <- function(median, q1, q3, n) {
mean_est <- (q1 + median + q3) / 3
sd_est <- (q3 - q1) / 1.35
list(mean = mean_est, sd = sd_est)
}
# Hozo et al. 2005 for median + range
median_range_to_mean_sd <- function(median, min, max, n) {
mean_est <- (min + 2*median + max) / 4
sd_est <- (max - min) / 4
list(mean = mean_est, sd = sd_est)
}
events ≤ total for all binary outcomesintervention_n + control_n ≤ total_n0 ≤ percentages ≤ 100ci_lower < estimate < ci_uppersd > 0 for continuous outcomescompile_binary <- function(extraction_dir) {
files <- list.files(extraction_dir, pattern = "\\.yaml$", full.names = TRUE)
rows <- list()
for (f in files) {
data <- yaml::read_yaml(f)
for (outcome in data$outcomes$primary) {
if (outcome$type == "binary") {
rows[[length(rows) + 1]] <- data.frame(
study = data$study_id,
year = data$year,
outcome = outcome$name,
events_int = outcome$intervention$events,
n_int = outcome$intervention$total,
events_ctrl = outcome$control$events,
n_ctrl = outcome$control$total
)
}
}
}
do.call(rbind, rows)
}
# Save pooled data
pooled <- compile_binary("extractions/")
write.csv(pooled, "extractions/pooled_binary.csv", row.names = FALSE)