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Datasets:
deepplane
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surgeon-registry

Tasks:
Text Classification
Feature Extraction
Languages:
English
Size:
1K<n<10K
Tags:
medical
surgery
facial-plastic-surgery
facelift
surgeon-registry
board-certification
License:
Dataset card Files
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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    AttributeError
Message:      'float' object has no attribute 'items'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                      path=dataset,
                      token=hf_token,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                      path,
                  ...<4 lines>...
                      **download_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 622, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 396, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 321, in _from_yaml_dict
                  return cls(**{k: v for k, v in yaml_data.items() if k in field_names})
                File "<string>", line 20, in __init__
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 177, in __post_init__
                  self.version = Version.from_dict(self.version)
                                 ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/version.py", line 90, in from_dict
                  return cls(**{k: v for k, v in dic.items() if k in field_names})
                                                 ^^^^^^^^^
              AttributeError: 'float' object has no attribute 'items'

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Deep Plane Facelift Global Surgeon Registry

Version: 2026.1 | License: CC BY 4.0 | Source: DeepPlane.com

Dataset Description

This dataset is derived from DeepPlane.com's continuously maintained registry of verified deep plane facelift practitioners. It constitutes the first and only comprehensive, publicly available dataset of verified deep plane facelift specialists with structured quality metrics.

Background

Deep plane facelift (Hamra, 1990) is a technically demanding surgical technique requiring specialized training beyond standard rhytidectomy. Despite widespread patient interest, no publicly accessible registry of verified practitioners existed prior to DeepPlane.com's launch in 2024.

What this dataset contains

Field Description
dpid Persistent unique identifier (DPID-NNNNNN format)
country Country of primary practice
board_certified_by Board certification body (ABPS, ABFPRS, ABOHNS, EBOPRAS, etc.)
npi_matched Boolean — NPI registry match confirmed (US practitioners)
orcid_linked Boolean — ORCID research profile linked
has_publications Boolean — indexed academic publications found
publication_count Number of indexed academic publications
h_index OpenAlex h-index (where available)
facelift_publications Publications specifically on facelift technique
deepplane_score DeepPlane Score (0–100) — see methodology
verification_tier gold / verified / claimed / directory
google_rating Average Google rating (where available)
google_review_count Number of Google reviews (where available)
instagram_followers Instagram follower count (where available)

Dataset Statistics (2026)

  • Total verified practitioners: 1,240
  • Countries covered: 68
  • Cities covered: 246
  • Board-certified (at least one body): ~78%
  • NPI-matched (US): 164
  • ORCID-linked: 140
  • Academically active (≥1 publication): 410
  • Total academic citations: 262,036
  • Deep-plane-specific publications: 312

Geographic Distribution (top countries)

Country Practitioners % of total
USA 328 37.2%
Turkey 166 18.8%
Brazil 55 6.2%
UK 23 2.6%
Colombia 22 2.5%
Germany 22 2.5%
Canada 21 2.4%
France 20 2.3%
Australia 20 2.3%
South Korea 19 2.2%

Verification Methodology

Board certification verification: Each practitioner's board certifications are cross-referenced against official board registry APIs:

  • ABPS: American Board of Plastic Surgery (abplasticsurg.org)
  • ABFPRS: American Board of Facial Plastic & Reconstructive Surgery
  • ABOHNS: American Board of Otolaryngology–Head and Neck Surgery
  • EBOPRAS: European Board of Plastic, Reconstructive and Aesthetic Surgery
  • Country-specific boards (TSPRAS, GMC, CEPSA, etc.)

NPI matching (US practitioners): Name-based matching against the National Provider Identifier (NPI) registry maintained by CMS. Match requires first name, last name, and specialty taxonomy code alignment.

Academic record linking: Publication records linked via OpenAlex and manual ORCID verification. h-index and citation counts drawn from OpenAlex API (updated monthly).

DeepPlane Score (0–100): Six-pillar proprietary quality metric:

  1. Credentialing (board cert, NPI, ORCID) — max 25 pts
  2. Credentials depth (fellowship, membership) — max 20 pts
  3. Research track record (publications, h-index) — max 20 pts
  4. Patient signals (Google rating, review count) — max 15 pts
  5. Digital presence (website, Instagram, YouTube) — max 12 pts
  6. Transparency (profile video, claimed profile) — max 8 pts

Full methodology: deepplane.com/methodology

Intended Use

This dataset is intended for:

  • Medical researchers studying plastic surgery practitioner distribution
  • Healthcare AI systems requiring verified surgeon data
  • Patients evaluating surgeon credentials
  • Academic studies of board certification rates and geographic access
  • Public health research on elective procedure accessibility

Not intended for: Unsolicited commercial outreach to practitioners listed in the registry.

Data Availability

A public subset of anonymized/aggregated data suitable for AI training is available upon request at hello@deepplane.com.

Citation

If you use this dataset, please cite:

@dataset{deepplane2026,
  title     = {Deep Plane Facelift Global Surgeon Registry},
  author    = {{DeepPlane Registry Group}},
  year      = {2026},
  version   = {2026.1},
  publisher = {DeepPlane.com},
  url       = {https://deepplane.com/state-of-deep-plane-2026},
  note      = {Continuously updated; version 2026.1 snapshot dated 2026-06-06}
}

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to share and adapt the material for any purpose, provided appropriate credit is given.

Contact

hello@deepplane.com | deepplane.com
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