Q (sonnet-5.5): What fraction of patients were older than five when diagnosed according to the Pediatric Neuroblastoma study from TARGET?

▶ list_studies
{
 "search": "TARGET neuroblastoma"
}

◀ result
{"result":[{"cancer_study_identifier":"nbl_target_gdc","name":"Neuroblastoma (TARGET GDC, 2025)","type_of_cancer_id":"nbl","sample_count":533,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=nbl_target_gdc"},{"cancer_study_identifier":"nbl_target_2018_pub","name":"Pediatric Neuroblastoma (TARGET, 2018)","type_of_cancer_id":"nbl","sample_count":1089,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=nbl_target_2018_pub"},{"cancer_study_identifier":"nbl_msk_2023","name":"Pediatric Neuroblastoma (MSK, Nat Genet 2023)","type_of_cancer_id":"nbl","sample_count":223,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=nbl_msk_2023"}]}

▶ read_guide
{
 "uri": "cbioportal://clinical-data-guide"
}

◀ result
# Clinical Data Query Guide

## Overview
Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis.

## Data Organization

### Patient-Level vs Sample-Level Data
- **Patient-level**: Demographics, overall survival, disease stage (stored once per patient)
- **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient)

### Key Tables
- `clinical_patient`: Patient-level clinical attributes
- `clinical_sample`: Sample-level clinical attributes
- `clinical_data_derived`: Pre-joined view combining both levels
- `clinical_attribute_meta`: Metadata about available clinical attributes

## Recommended Approach: Use clinical_data_derived

The `clinical_data_derived` table is pre-joined and optimized for most queries:

```sql
-- Get clinical data for specific attributes
SELECT
    sample_unique_id,
    patient_unique_id,
    attribute_name,
    attribute_value
FROM clinical_data_derived
WHERE
    cancer_study_identifier = 'your_study_id'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS');
```

## Clinical Attribute Discovery

### Use clinical_attribute_meta for Discovering Available Attributes
**Always start here** to see what clinical attributes are available for a specific study:

```sql
-- Discover available clinical attributes for a study
SELECT
    attr_id,
    description,
    patient_attribute,
    cancer_study_id
FROM clinical_attribute_meta
WHERE
    cancer_study_id = (
        SELECT cancer_study_id
        FROM cancer_study
        WHERE cancer_study_identifier = 'your_study_id'
    )
ORDER BY patient_attribute, attr_id;
```

**Key Fields:**
- **attr_id**: matches attr_id in clinical_sample/clinical_patient tables
- **description**: human-readable description of the attribute
- **patient_attribute**: true = patient attribute, false = sample attribute
- **cancer_study_id**: links to cancer_study table (filter by study)

## Attribute Semantics and Matching

### Case-Insensitive Matching for Attribute Values

Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant.

When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study:

```sql
-- Correct: case-insensitive clinical value filter
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'your_study_id'
  AND attribute_name = 'MUTATION_STATUS'
  AND upper(attribute_value) = 'GERMLINE';
```

Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study.

### Query the Requested Attribute, Not a Proxy

Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars)

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_name, count() n, uniq(patient_unique_id) p, min(toFloat64OrNull(attribute_value)) mn, max(toFloat64OrNull(attribute_value)) mx FROM clinical_data_derived WHERE cancer_study_identifier='nbl_target_2018_pub' AND (attribute_name ILIKE '%AGE%' OR attribute_name ILIKE '%BIRTH%' OR attribute_name ILIKE '%DIAG%') GROUP BY attribute_name"
}

◀ result
{"rows":[{"attribute_name":"AGE","n":1076,"p":1076,"mn":0,"mx":21},{"attribute_name":"PERCENTAGE_NECROSIS","n":1076,"p":1076,"mn":0,"mx":100},{"attribute_name":"INSS_STAGE","n":1076,"p":1076},{"attribute_name":"AGE_IN_DAYS","n":1076,"p":1076,"mn":0,"mx":7612},{"attribute_name":"DIAGNOSIS","n":1076,"p":1076},{"attribute_name":"PERCENTAGE_TUMOR_PURITY","n":1076,"p":1076,"mn":0,"mx":100},{"attribute_name":"YEAR_OF_DIAGNOSIS","n":1076,"p":1076,"mn":1986,"mx":2012}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT count() total, countIf(d>1826.25) over5_days, countIf(a>5) over5_agecol, countIf(a>=5) ge5_agecol, countIf(d IS NULL) missing FROM (SELECT patient_unique_id, anyIf(toFloat64OrNull(attribute_value), attribute_name='AGE_IN_DAYS') d, anyIf(toFloat64OrNull(attribute_value), attribute_name='AGE') a FROM clinical_data_derived WHERE cancer_study_identifier='nbl_target_2018_pub' AND attribute_name IN ('AGE','AGE_IN_DAYS') GROUP BY patient_unique_id)"
}

◀ result
{"rows":[{"total":1076,"over5_days":125,"over5_agecol":125,"ge5_agecol":251,"missing":0}]}

═ answer (success)
In the Pediatric Neuroblastoma (TARGET, 2018) study, **125 of 1,076 patients (about 11.6%)** were older than 5 years at diagnosis. That is roughly 1 in 9. Every patient in the study has an age recorded.

- **Age used:** Age at diagnosis in days, with "older than 5" meaning more than 1,826 days (5 × 365.25). The study's age-in-years column gives the same count.
- **Exactly age 5:** A further 126 patients were recorded at exactly 5 years. If you count age 5 as "older than five", the number is 251 of 1,076 (about 23%).
- **Which study:** Two other TARGET neuroblastoma datasets exist, "Neuroblastoma (TARGET GDC, 2025)" and the MSK neuroblastoma study. I used the 2018 study because its name matches yours.

[View the study in cBioPortal](https://www.cbioportal.org/study/summary?id=nbl_target_2018_pub)