CPSC's SREM Identification Blueprint

2. Deciphering the CPSC's Criteria for SREM Identification

The NEISS database stands as a testament to the CPSC's commitment to transparency. However, the CPSC occasionally leaves analysts in the dark about the rationale behind their categorization of incidents for regulatory purposes. This makes the 72 SREM incidents identified during 2009-2012 a valuable resource for comprehension (https://www.federalregister.gov/d/2012-21608/p-119). The CPSC's description of these incidents alludes to "high-powered and/or ball-shaped magnet ingestions" that potentially pertain to the magnets in question. Still, the actual selection criteria employed by the CPSC is neither straightforward nor uniformly applied.

As seen in the spreadsheet, estimates for identifying SREM are more nuanced than just searching for "MAGN" or "BATT". Drawing insights from the CPSC's approach during 2009-2012, we deduce a two-step general strategy:

1. Prematching: Of all magnet incidents, do a narrative search for words relating to attributes such as roundness, high magnetic power, or use in or around mouth. We accomplish this step basic spreadsheet formulas.

2. Subjective Filtering: Manually filter out mismatches, relying on discernment. For instance, incidents might be discounted if they refer to non-magnetic steel balls or if the context suggests irrelevance, such as a match for "RUBBING" when searching for "BB". A GPT-Quorum is used well suited in accomplishing this step quickly and reliably.

Detailed Implementation of the SREM Identification Strategy

Step 1: Prematching

An algorithmic solution similar to MAGN identification (See 2022 NEISS data overview) can facilitate this step, with spreadsheet software like Google Sheets or Excel serving as a useful tool. Within the MAGN incident data set, identify any incidents with the following terms: POWER, RARE, MARB, BALL, BB, BEARING, BEAD, SPHER, ROUND, PIERCE, TONGUE, TOUNG, LIP. Each of these can be flagged in their own column for visual overview, or the following combined formula can check all keywords at once. For example:

=IF(OR(ISNUMBER(SEARCH("POWER",W1)), ISNUMBER(SEARCH("RARE",W1)), ISNUMBER(SEARCH("MARB",W1)), ISNUMBER(SEARCH("BALL",W1)), ISNUMBER(SEARCH("BB",W1)), ISNUMBER(SEARCH("BEARING",W1)), ISNUMBER(SEARCH("BEAD",W1)), ISNUMBER(SEARCH("SPHER",W1)), ISNUMBER(SEARCH("ROUND",W1)), ISNUMBER(SEARCH("PIERCE",W1)), ISNUMBER(SEARCH("TONGUE",W1)), ISNUMBER(SEARCH("TOUNG",W1)), ISNUMBER(SEARCH("LIP",W1))), 1, 0)

Step 2: Subjective Filtering

Injecting subjectivity into a classification method requires finesse, especially if the goal is to mimic the CPSC's approach while ensuring reproducibility. To this end, we harnessed the capabilities of AI, with the use of a GPT-4 . A detailed tutorial on how and when to implement as GPT-4 quorum is available (here). In summary, AI does an excellent job discerning which narratives align with SREM criteria and which don't, based on a set of predefined rules and exceptions found to mimic the behavior of CPSC staff in 2012. 

From the set of all Prematched incidents found in Step 1 (See Column AP, MAGN_Only sheet), the following Workspace GPT formula merges instruction prompt with narrative content from cell W24:

GPT Workspace Formula

=GPT(CONCATENATE("The instructions below normally require a brief output, but I would like you to explain your thinking, step by step

Task: Evaluate a NEISS incident narrative related to a potential ingestion of a rare earth spherical magnet set, such as buckyballs.

Response Format:

If the ingestion matches any exclusion criteria, respond with only a two-word reason. Do not provide any explanation or use additional words in your response.

Otherwise, if the ingestion doesn't match any exclusion criteria, respond with only the number: 1.

Exclusion Criteria (Reasons):

A. Metal Ball: If the ball ingested is metal or steel, often as a part of a magnetic product known to use metal balls (like geomag, magnetix for example). Consider that multiple objects may have been ingested, in which case the 'Metal Ball' exclusion criteria applies if the the ball ingested is not described as a magnetic ball. EXCEPTION: In case the ingested ball has a contradictory description like 'MAGNETIZED METAL BEAD' or 'METAL MAGNETIC BALL', do not use 'Metal Ball' exclusion criteria and output 1.

B. Non Magnetic: If the ingested ball is non magnetic. May refer to non-magnetic balls or a component of a decoration involving magnets but is not itself magnetized (e.g., a ball off an old magnet decoration or a part of a magnetic toy set not described as magnetic itself).

C. Shape Mismatch: If the magnet ingested specifically isn't spherical in shape, like a magnetic tube, cylinder, or disc.

D. Bracelet: The ingested item is explicitly part of a bracelet or similar wrist jewelry.

E. Not Ingested: If narrative doesn't indicate swallowing of a round and/or strong magnet. Magnets inserted in the nostrils are not considered ingestions, for example.

F. Uncertain Ingestion: If the narrative expresses uncertainty about the existence of an ingestions.

There might be other unique situations not covered by the above criteria, in which case come up with a creative 2 word reason for the exclusion. Remember, there might also be instances where multiple objects are ingested simultaneously. Your output should either a two word explanation like 'Metal Ball' if excluded, or '1' if not excluded.

Now, please take a deep breath and slowly evaluate the following NEISS narrative:

", $W24))

This prompt was found to mimic the prevailing behavior of the CPSC's selection of the 72 SREM incidents as closely as possible. Querying GPT-4 to think "step by step", "explain your thinking", and "take a deep breath" dramatically increases accuracy, and leaves a trail of thought that can be retrospectively analyzed. An example output of this prompt, as seen in cell AT24:

Step 1: Read the narrative: "INGESTION METAL BALL - 4 YOM INGESTED A SMALL METAL MAGNET BALL AT SCHOOL THIS MORNING, NO TROUBLE BREATHING."

Step 2: Identify the key information: The narrative states that a 4-year-old male ingested a small metal magnet ball at school. It also mentions that there is no trouble breathing.

Step 3: Apply the exclusion criteria:

A. Metal Ball: The narrative explicitly mentions that the ingested object is a "small metal magnet ball." Therefore, this exclusion criterion does not apply.

B. Non Magnetic: There is no information suggesting that the ingested ball is non-magnetic, so this exclusion criterion does not apply.

C. Shape Mismatch: The narrative describes the ingested object as a ""small metal magnet ball,"" which indicates a spherical shape. Therefore, this exclusion criterion does not apply.

D. Bracelet: The narrative does not mention anything about the ingested item being part of a bracelet or similar wrist jewelry. Therefore, this exclusion criterion does not apply.

E. Not Ingested: The narrative explicitly states that the ball was ingested. Therefore, this exclusion criterion does not apply.

F. Uncertain Ingestion: The narrative does not express any uncertainty about the existence of an ingestion. Therefore, this exclusion criterion does not apply.

Step 4: Determine the output: None of the exclusion criteria apply, so the output should be the number 1 to indicate that the ingestion does not match any exclusion criteria.

Final output: 1

Another GPT-4 Prompt can fetch only the output for spreadsheet analysis:

=GPT(CONCATENATE("Return the final from an explanation. The output will either be the single digit '1' or a two word explanation like 'metal ball', and respond with nothing else beside the output. Here is the explanation to retrieve the output from:

", $AT24))

To mitigate any inconsistencies, each narrative underwent evaluations by three separate instances of GPT-4, with the majority verdict dictating the final classification. Though there are many ways to implement this GPT-4 automation, we specifically use the [GPT Quorum method explained here]. An example output of our GPT Quorum run can be found column AR of the "MAGN_Only" sheet. 

Much like in humans, a high congruence rate among the GPT quorum doesn't automatically imply correctness or fairness in decisions. Nonetheless, uniform decisions indicate that the classification guidelines are more stringent, providing precise and unambiguous criteria. The prompts provided were crafted to mirror the CPSC's dominant behavior observed in the 72 SREM incidents, as marked in the "CPSC" Column AQ of the "MAGN_Only" sheet. Out of the 783 Prematches reviewed, 606 decisions had complete agreement within the GPT Quorum. Utilizing the same prompts should yield a comparable 82% consensus ratio; however, the specific cases with dissent will differ.